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 PRIVACY AND CIVIL LIBERTIES OVERSIGHT BOARD

 

 Defining Privacy Forum

 

 November 12, 2014

 

 

The public forum was held at the Georgetown 

Marriott Hotel, 1221 22nd Ave, N.W., Washington, 

D.C. commencing at 8:30 a.m.

 

 

 

Reported by: Lynne Livingston
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1 BOARD MEMBERS

2

3 David Medine, Chairman

4 Rachel Brand

5 Patricia Wald

6 James Dempsey

7 Elizabeth Collins Cook

8

9 SESSION 1

10 Defining Privacy Interests

11 

12 Ed Felten, Princeton University

13 Liza Goitein, Brennan Center for Justice

14 Paul Rosenzweig, Red Branch Consulting, PLLC

15 Dan Solove, George Washington University Scool of 

Law

16

17 

18 

19 

20 

21 

22 
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1 SESSION 2

2 Privacy Interests in the Countertrrorism Context

 and the Impact of Technology

3

Annie Anton, Georgia Tech University

4

Alvaro Bedoya, Geogretown Center on Privacy & 

5 Technology

6 Michael Hintze, Microsoft

7 Hadi Nahari, Chief Security Architect, NVIDIA

8

9 SESSION 3

10 Privacy Interests Identified and Addressed 

 by Government Privacy Officials

11

Alex Joel, Office of the Director of National 

12 Intelligence

13 Erika Brown Lee, U.S. Department of Justice

14 Rebecca Richards, National Security Agency

15

16 SESSION 4

17 Applying Lessons Learned from the Private Sector

18 Fred Cate, Indiana University School of Law

19 Harley Geiger, Center of Democracy and Technology

20 Chris Inglis, Paladin Capital Group and former 

Deputy Director of NSA 

21 

22 
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1 PROCEEDINGS

2 MR. MEDINE: Good morning, and welcome 

3 to the Privacy and Civil Liberties Oversight 

4 Board's public meeting on defining privacy.

5 It's 8:30 a.m. on November 12th, 2014, 

6 and we're meeting in the west-end ballroom in the 

7 Washington Marriott Georgetown Hotel in 

8 Washington, D.C. 

9 This hearing was announced in the 

10 Federal Register on October 21st, 2014. As 

11 chairman, I will be the presiding officer. 

12 All five Board members are present and 

13 there is a quorum. The Board members are Rachel 

14 Brand, Elisebeth Collins Cook, James Dempsey and 

15 Patricia Wald. 

16 I will now call the hearing to order. 

17 All in favor of opening the hearing, please say 

18 aye.

19 (Vote taken.)

20 MR. MEDINE: We will proceed.

21 So what is privacy? The right to be 

22 left alone? A desire for independence of 
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1 personal activity? The right to make decisions 

2 regarding one's private matters? Space for 

3 intellectual development, anonymity or obscurity? 

4 Freedom from public attention? Freedom from 

5 being observed or disturbed by others? Freedom 

6 from intrusion into one's solitude? Avoiding 

7 public disclosure of private facts about 

8 yourself? Freedom from publicity which places 

9 you in a false light? Freedom from appropriation 

10 of your name or likeness? Control of how one's 

11 personal information is collected and used? 

12 Freedom from surveillance. 

13 These are just a few definitions that 

14 have been given to privacy in the past. I expect 

15 during the course of today's discussion that 

16 we'll hear others. 

17 The meeting today and the comments we 

18 receive will inform the Board's approach to 

19 privacy issues within its statutory mandate. 

20 There will be four panels today. The 

21 first will focus on defining privacy interests. 

22 The second will consider privacy interests in the 
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1 counterterrorism context and the impact of 

2 technology. 

3 Next we will hear from government 

4 privacy officials regarding privacy interests 

5 that have been identified and addressed. The 

6 final panel will see how lessons learned from the 

7 private sector can be applied in the 

8 counterterrorism context. 

9 Each panel will be moderated by a 

10 different Board member, and after the Board 

11 member poses questions other Board members will 

12 have the opportunity to pose questions.

13 And afterwards, members of the audience 

14 are invited to submit written questions. Peter 

15 Winn has cards and people can get a card from him 

16 and submit the questions, time permitting, for 

17 the moderator to pose to the panelists. 

18 I want to thank the panelists who have 

19 agreed to appear here today on this panel and 

20 others. 

21 I also want to note that we have a 

22 strict timekeeper, Joe Kelly, sitting in front, 
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1 and so panelists are encouraged to keep their 

2 remarks brief so we can have a more extensive 

3 discussion. 

4 We will be taking a lunch break between 

5 noon and 1:15. 

6 Today's program is being recorded and a 

7 transcript will be prepared and put on our 

8 website at plcob.gov in a week or so. 

9 Written comments from members of the 

10 public are also welcome and may be submitted 

11 through regulations.gov through the end of the 

12 year.

13 Finally, I want to thank the Board's 

14 staff, Sharon Bradford-Franklin, Shannon Wilson, 

15 Simone Awang, Lynn Parker Dupree, Renee 

16 Gewercman, Peter Winn, Joe Kelly for their 

17 efforts in making today's event possible.

18 And so we'll now turn to the first panel 

19 moderated by Judge Wald.

20 MS. WALD: Thank you. Panel 1 will 

21 attempt to explore, I think it would be too 

22 ambitious to say define privacy, and the many 
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1 separate individual and societal interests that 

2 the notion of privacy encompasses. 

3 The novelist Jonathan Franzen 

4 perceptively remarked, "Privacy is like the 

5 Cheshire cat of values, not much substance but a 

6 very winning smile. Legally, the concept is a 

7 mess." That's a quote. 

8 That may be unduly pessimistic. Most 

9 commentators do agree that there are aspects of 

10 privacy that go way back to the most ancient 

11 civilizations, and that our own Founding Fathers 

12 enshrined several of them in the Bill of Rights.

13 But the concept of privacy has become a 

14 receptacle for a conglomerate of interests or 

15 values that individuals and society care about, 

16 but which to varying degrees they're willing to 

17 balance with competing values, such as national 

18 security.

19 Thus, the law of privacy consists mainly 

20 of a series of situations in which courts, 

21 legislators or government officials have decided 

22 to recognize a privacy interest, or not to, and 
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1 to protect, or not to, that interest against a 

2 competing value.

3 So our panelists today will identify the 

4 varied individual and societal interests that 

5 travel under the rubric of privacy and discuss 

6 how far and under what conditions our laws do or 

7 should legitimate claims that are based upon 

8 those particular interests.

9 Now our format will be for each panelist 

10 to talk initially for seven minutes. And the 

11 gentleman in the front row will give you a yellow 

12 card two minutes before, and a green card will 

13 mean it's time to wrap up. 

14 And then at the end of their initial 

15 speeches, then I will question them as the 

16 moderator for about 20 minutes. Then that will 

17 be followed by another 20 minutes of questions by 

18 my fellow Board members. 

19 After that, and I hope there will be 

20 some time left for the written questions which 

21 members of the audience are invited to send to 

22 the people who circulate to collect them, and 
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1 then I will question them. I will discuss some 

2 of those questions with the people on the panel. 

3 You already, I think, have bios of our 

4 illustrious panelists, but I'm going to identify 

5 them very briefly before they speak.

6 So we'll start off, Liza Goitein is a 

7 Co-Director of the Brennan Center's Liberty and 

8 National Security Program. 

9 MS. GOITEIN: Thanks very much, Judge 

10 Wald. And I apologize in advance, I have a cold 

11 so my voice kind of comes and goes, but thank you 

12 to all of the Board members for inviting me to 

13 participate in today's discussion. 

14 If there's one thing I've learned from 

15 my own involvement in privacy issues over the 

16 past few years is that privacy is different 

17 things to different people. 

18 David gave a very comprehensive list of 

19 some of the things that privacy is. I'm not sure 

20 what I would add to that, except to say that I 

21 think that for those who are outside the 

22 ideological mainstream in this country, privacy 
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1 vis-a-vis the government can be critical to 

2 effectuate other rights, such as the freedom to 

3 religion, speech and association.

4 So collectively as a society we value 

5 all of those aspects of privacy, even if some of 

6 us value only some of them, or none of them.

7 So what does that mean for our analysis? 

8 I think it's interesting for us to think about 

9 different definitions of privacy, and it's 

10 helpful insofar as it shows the range of 

11 definitions that are out there. 

12 But I'm not at all convinced that 

13 Congress, or the courts, or this Board should be 

14 in the business of attempting a granular 

15 definition of privacy or its importance. 

16 Look at the freedom of religion, by way 

17 of comparison. Courts don't probe what religion 

18 is or why it's important. And that's not because 

19 the definition of religion is obvious, by any 

20 means. It's at least in part because of the 

21 opposite, because religion is different things to 

22 different people. 
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1 So what the court does is it adopts a 

2 concept of religion that's broad enough to 

3 encompass the many different roles that religion 

4 plays in people's lives, and then the court 

5 protects it, except in the rare circumstance 

6 where there's an overriding governmental 

7 interest. And Congress has followed the same 

8 approach. 

9 When it comes to information privacy, 

10 which is what I focus on in my job, the best 

11 working concept of privacy, the concept that best 

12 encompasses all of the important interests that 

13 privacy serves, is control of information.

14 This concept avoids to some extent the 

15 what and the why of privacy, and focuses instead 

16 on the how, how privacy is realized as a 

17 practical matter. 

18 And it also has the additional advantage 

19 of matching up quite well with the text of the 

20 Fourth Amendment. If a person controls her 

21 papers, she is secure in them. If a person does 

22 not control her papers, she is not secure in 
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1 them. 

2 What are some of the ramifications of 

3 this concept of privacy? Well first, controlling 

4 one's information means controlling not only what 

5 one shares, but with whom and under what 

6 circumstances. 

7 I may share certain information with my 

8 mother or with a close childhood friend, but that 

9 doesn't mean that I've chosen to share that 

10 information with the entire world, including the 

11 NSA. 

12 Sure, there's a chance my mother might 

13 rat me out. There's a chance that my childhood 

14 friend has a tax problem I didn't know about and 

15 could be pressured by the government into 

16 becoming an informant. 

17 But to equate this outside risk that my 

18 confidences may be misplaced, with a willing 

19 disclosure to everyone in the world is a legal 

20 fiction of the worst kind, and that's really what 

21 the third-party doctrine is, in my view. 

22 Second, you don't, in fact, relinquish 
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1 all control over information about your public 

2 activities by virtue of walking out your front 

3 door. There is such a thing, functionally 

4 speaking, as privacy in public. 

5 And this is something that's 

6 well-understood in the FOIA context, the Freedom 

7 of Information Act context. There's a privacy 

8 exception under FOIA which allows the government 

9 to withhold information if releasing it would 

10 unduly compromise personal privacy. Think of 

11 Social Security records. 

12 The Supreme Court held in 1989, that a 

13 rap sheet would be covered by this exemption, 

14 despite the fact that all of the information in a 

15 rap sheet is available by virtue of a diligent 

16 door-to-door combing of court records. 

17 So why was the rap sheet still private? 

18 Because the court held while the information in 

19 it was publicly available, it was practically 

20 obscure. 

21 This is such a commonsense concept and 

22 it deserves a home in Fourth Amendment 
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1 jurisprudence. The sum total of a person's 

2 movements in public over extended periods of time 

3 may be publicly available information, but using 

4 normal powers of human observation it is 

5 practically obscure.

6 So when the government uses drones, or 

7 stingrays, or GPS technology to pierce that 

8 obscurity, it has compromised the control that 

9 the person would otherwise exercise over this 

10 information, and that's a privacy violation. 

11 Third, privacy violations happen at the 

12 point that the information is collected. We've 

13 heard intelligence officials recently telling us 

14 that we don't have to worry about the NSA's 

15 collection of bulk collection of telephone 

16 records because nobody looks at the records 

17 unless they have reason to suspect some kind of 

18 terrorist link. That is the government telling 

19 you what aspects of privacy you should value. 

20 Many people won't care if the government 

21 collects but doesn't look. Other people won't 

22 care if the government looks but doesn't 
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1 prosecute. 

2 But the point at which the government 

3 collects the information is the point at which 

4 you've lost control. And for plenty of people 

5 that loss of control itself produces harm. It 

6 produces a feeling of vulnerability. It causes 

7 people to change their behavior.

8 In 2014, there was a poll after the 

9 Snowden disclosures showing that 47 percent of 

10 respondents had changed their online behavior 

11 after those disclosures.

12 There was another survey of 520 American 

13 writers showing that one out of six authors, 

14 after the Snowden disclosures, refrained from 

15 writing about certain topics because they feared 

16 surveillance.

17 After news stories broke about the 

18 NYPD's infiltrations of Muslim student 

19 associations, attendance in those associations 

20 dropped. 

21 In some ways these are some of the worst 

22 harms that come from privacy violations because 
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1 they're society-wide. They impact the way we 

2 function as a society. They impoverish our 

3 social discourse by causing people to sensor 

4 themselves and not put ideas out there. 

5 One last ramification of this concept of 

6 privacy -- if I have time, I can't believe I have 

7 time -- is young people. So I hear it said quite 

8 often that young people don't care about privacy. 

9 And it's certainly true that many young people go 

10 on Facebook and share incredibly personal 

11 information with 622 friends. But they don't 

12 share that information with 623 friends. 

13 What they share and the number of people 

14 that they share it with may very well have 

15 changed, it certainly appears so, but they still 

16 control the sharing, or at least they think they 

17 do. 

18 And my impression, based on a totally 

19 unscientific survey of all the young people in my 

20 life, is that they still value that control. 

21 So, the red card, I knew it was coming. 

22 All right, I'll stop there.
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1 MS. WALD: Okay, thank you. Professor 

2 Daniel Solove is the John Marshall Harlan 

3 Research Professor of Law at the George 

4 Washington Law School.

5 MR. SOLOVE: Good morning. I would like 

6 to make five brief points this morning. 

7 The first point is that privacy is much 

8 more than hiding bad secrets. One of the common 

9 arguments that people often make about privacy is 

10 that people shouldn't worry if they have nothing 

11 to hide. And I hear this argument all the time. 

12 This argument, and many other arguments 

13 about privacy, are based on a conception of 

14 privacy, a conception of privacy that's very 

15 narrow, that sees privacy as hiding bad or 

16 discreditable things. 

17 Well, privacy is much more than that. 

18 Privacy isn't just one thing, it's many different 

19 things. Privacy involves keeping people's data 

20 secure. It involves the responsible use of data. 

21 It involves making sure that when data 

22 is kept, it's kept accurately. It's making sure 
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1 that people who keep the data are responsible 

2 stewards of that data, that people have rights in 

3 that data and some participation in the way that 

4 data is used. 

5 All these things have nothing to do with 

6 nothing to hide. They have nothing to do with 

7 secrets and everything to do with how their 

8 information is kept, collected, stored, etcetera. 

9 I think that if we see privacy broadly 

10 we can move away and abandon these very narrow, 

11 cramped views of privacy. 

12 The second point I'd like to make is 

13 that privacy is a societal interest, not just an 

14 individual one. 

15 When balancing privacy and security, 

16 privacy is often seen as an individual right and 

17 then security is often seen as a social right. 

18 And when they're balanced, society generally wins 

19 out over the individual. And I think this 

20 actually skews the balance to the society side, 

21 to the security side. 

22 But, in fact, privacy isn't just an 
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1 individual interest. It doesn't just affect the 

2 individual, it's a societal interest. We protect 

3 privacy because we want to protect society. We 

4 want to shape the kind of society we want to live 

5 in. 

6 Privacy doesn't just protect the 

7 individual for the individual's sake, it protects 

8 the individual for the society's sake, because we 

9 want a free society where people are free to 

10 think and speak without worrying about negative 

11 consequences from that. 

12 The third point I'd like to make is that 

13 the collection of personal data through 

14 surveillance and other means of government 

15 information gathering can cause significant 

16 problems. 

17 Data collection and surveillance aren't 

18 inherently bad, but just as industrial activity 

19 causes pollution, government surveillance and 

20 data gathering can cause problems. And these 

21 problems must be mitigated. They must be 

22 addressed when they clash with important 
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1 interests. 

2 Some of the problems include, one, that 

3 this activity can chill people's expression. It 

4 can chill people's exploration of ideas. It can 

5 chill people in many different ways. Either they 

6 might not say something, or they might say 

7 something slightly differently, or they might act 

8 differently, or do things differently. We don't 

9 want that chilling when it comes to legal 

10 activity. 

11 The other thing, the other problem, is 

12 that surveillance gives a lot of power to the 

13 watchers. There's a lot of things that can be 

14 done with a vast repository of data beyond a 

15 particular aim that it might have been collected 

16 for. Data has a way of often being used in other 

17 manners, in other ways. 

18 I think that another issue too is the 

19 level of accountability and oversight that goes 

20 into this, because it's about the structure of 

21 our government and the relation of the government 

22 to the people that we're talking about here. 
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1 What kind of accountability will the 

2 government have when it gathers all this 

3 information? What limits will there be on the 

4 information gathered and used? How long will the 

5 information be kept? 

6 In a free society people are free to act 

7 as they want to act, as long as it's within the 

8 bounds of the law without having to justify 

9 themselves. 

10 They don't have to go and explain their 

11 actions to a bureaucrat sitting in a room full of 

12 television monitors about what they're doing. 

13 They don't have to go and explain themselves when 

14 a computer's lights are blinking red because of 

15 something that they said and it can be 

16 misinterpreted. 

17 People don't have to worry about that. 

18 They can act freely without having to worry about 

19 how suspicious their actions might look. That is 

20 a key component to freedom.

21 The fourth point I'd like to make is 

22 that we can't adequately balance privacy and 
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1 security without a reasonable amount of 

2 transparency. 

3 There's an overarching principle that 

4 this nation was founded upon, it is that we the 

5 people are the boss. The government is our 

6 agent. We can't evaluate what government 

7 officials are doing if we don't know what's going 

8 on. 

9 Now this doesn't mean there should be 

10 absolute transparency, but it does mean that we 

11 need to know something, enough to be able to 

12 evaluate government surveillance. 

13 Because ultimately the choice about the 

14 proper level of surveillance isn't the NSA's to 

15 make, it's not the President's to make, it's the 

16 people's choice. We can't forget that. It's the 

17 people's choice, and the people must be given 

18 sufficient information to make that choice. 

19 My last point is that the government 

20 must get buy-in from the people for its 

21 surveillance measures. Without buy-in, people 

22 are going to start to take self-help measures, 
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1 which is something that we see happening now. 

2 We see that companies are providing 

3 people with ways to encrypt their data to protect 

4 it from snooping government entities. This is 

5 the market speaking. This is something that 

6 people want. This is something being sold to 

7 people that people are going to buy. This is 

8 something in demand. 

9 Why? Why are people demanding this? 

10 Because they've lost trust, because the laws 

11 regulating government surveillance are weak and 

12 do not provide adequate oversight or 

13 accountability. 

14 This is why strong privacy protections 

15 aren't necessarily bad for security. In fact, 

16 they ensure that the people are comfortable that 

17 there is adequate oversight and accountability 

18 for that surveillance and that they're 

19 comfortable and know that they have the 

20 information that they need to continually 

21 evaluate what's going on. 

22 And if they can evaluate what's going on 
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1 and buy into what's going on, things will be a 

2 lot better when it comes to balancing privacy and 

3 security. Thank you.

4 MS. WALD: Paul Rosenzweig is the 

5 founder of the Red Branch Consulting Program, and 

6 a senior advisor to the Chertoff Group, and he 

7 was formerly Deputy Assistant Secretary for 

8 Policy at the Department of Homeland Security.

9 MR. ROSENZWEIG: Thank you, Judge Wald, 

10 and thank you, Mr. Chairman and members of the 

11 Board. I appreciate the opportunity to speak 

12 with you today. 

13 It's really entirely appropriate for the 

14 Board to begin a discussion of privacy in this 

15 new technological age. In fact, in my judgement 

16 it's essential. 

17 And the reason for that is essentially 

18 one that puts me in some disagreement with my 

19 fellow panelists. I think that our conceptions 

20 of privacy, founded as they were back in the 

21 1970s with the FIPPs, are somewhat outdated and 

22 antiques that don't survive the technological 
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1 challenges that we face.

2 The 1973 Thunderbird was a marvelous car 

3 but we don't think of holding it out today as the 

4 state of automotive engineering. Nor would I 

5 think we should address the FIPPs as the state of 

6 privacy thinking. We need, in effect, a Tesla 

7 for privacy today. 

8 What would that look like? Well, there 

9 are many ways to answer that question, and I 

10 think to answer it you have to begin by thinking 

11 about what sort of value privacy is. 

12 And here, again, I think I find myself 

13 in some disagreement with other members on the 

14 panel and perhaps with members of the Board. I 

15 do not think that privacy is an ontological 

16 value. I don't think it's akin to religion. 

17 It's not an inherent human right or the product 

18 of some natural law.

19 Rather in my judgment privacy is an 

20 inherently instrumental value, one that acts in 

21 the service of other societal values. It's a 

22 utilitarian value that derives its worth only 
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1 insofar, in my judgment, as it fosters other 

2 positive social gains. 

3 Privacy for its own sake is just an 

4 assertion of autonomy from society. It is 

5 valuable insofar as it advances other objectives. 

6 Now let me kind of put some salt on 

7 that. The problem is that buried in the word 

8 privacy are many different social values that 

9 we're fostering, too many really to catalogue, 

10 though the chairman did a good job of trying to 

11 start. 

12 For example, we often see in the 

13 discussion here privacy is enhancing our freedom 

14 from government observation. That's probably the 

15 use that's most salient to what the Board does.

16 But it also enables democracy. That's 

17 why we keep the ballot private. It fosters 

18 personal morality. That's why we keep the 

19 confessional private. 

20 Privacy is also about restraining 

21 government misbehavior, which is why we see 

22 privacy values in the Fourth Amendment and other 
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1 procedural limitations on government action, 

2 another way in which privacy is obviously 

3 relevant to this Board. 

4 And it's also, as Dan said, sometimes 

5 about transparency in the sense that we have 

6 privacy rules so that I know what you know about 

7 me. 

8 It can be about control, about control 

9 of my own image. 

10 And it's sometimes also about simply 

11 shame, since one ground of privacy is enabling me 

12 to keep from the world things that I'm not proud 

13 I did, of which there are far too many, I fear. 

14 What's important to note is that in all 

15 of these instances the value that we're 

16 protecting that underlies privacy is different 

17 from the privacy itself. 

18 And that in turn suggests to me that the 

19 way to think about privacy is to think about what 

20 operational activities would protect the 

21 underlying value most.

22 It means we need to go to a micro level 
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1 to understand in general the nuance that arises 

2 from the particular interest that is at the core 

3 of the privacy that we're talking about. 

4 For example, we protect the 

5 confidentiality of attorney client 

6 communications. Why? Because we think we need 

7 to foster candor in the discussion between a 

8 client and an attorney. That's something that we 

9 feel so strongly about that the instances in 

10 which we permit that privacy to be violated are 

11 few and far between, and they come only with the 

12 highest level of judicial scrutiny.

13 The Fourth Amendment itself reflects a 

14 similar utilitarian value of the security of our 

15 persons, places and things against intrusion. 

16 Once again, we impose a high bar, a probable 

17 cause requirement and a strong independent 

18 outside adjudicator, a judge issuing a warrant. 

19 But those aren't the only mechanisms by 

20 which we can protect privacy. We have a series 

21 of administrative processes that are often 

22 adequate to protect and restrain government 
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1 observation. 

2 They're embedded in many of the internal 

3 reviews that are very common in the IC, in the 

4 intelligence community that you spend your time 

5 reviewing. 

6 They're common in virtually every 

7 institution of government that we have, at least 

8 at the federal level that I'm familiar with, 

9 where we think that administrative review, 

10 internal oversights, inspectors general, 

11 intelligence committee oversight are adequate 

12 alternate administrative mechanisms. 

13 So what does that mean for some of the 

14 things that you think about? Let me look at the 

15 two programs that you've written about and just 

16 kind of express something there. 

17 The 215 program is one that directly 

18 impacts issues of government abuse or potential 

19 abuse because of the pervasiveness of the 

20 collection that underwent, that was there. 

21 It strikes me that that sort of 

22 pervasive collection is one that would require a 
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1 strong, independent review mechanicism because of 

2 the comprehensiveness of its activity.

3 By contrast, the 702 program, which 

4 seems from what I've read from the outside from 

5 your reports, more narrowly focused, is one in 

6 which less error correction mechanisms are 

7 necessary, less likelihood of inadvertent abuse 

8 is there. 

9 So if you press on what is being 

10 protected, you get a sense of a better way to 

11 protect it. 

12 Let me say one brief word more about 

13 transparency. I completely agree with others on 

14 the panel that transparency is essential to 

15 control conduct and misconduct. 

16 But the critical question is, what type 

17 of transparency? And for me, again, this 

18 requires us to ask what transparency is for. 

19 It's the ground of oversight and audit. 

20 Transparency without that ground is just 

21 voyeurism. 

22 But absolute transparency, as Dan said, 
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1 can't be squared with the need for secrecy in 

2 operational programs. 

3 I sometimes think that some calls for 

4 transparency, though I hasten to say not by any 

5 other members of the panel or on the Board, are 

6 really just coded efforts to discontinue 

7 surveillance programs altogether.

8 The truth is that if we believe in 

9 absolute transparency, we've gone a long way to 

10 the view that democracies can't have secrets, a 

11 view which I think is untenable in the modern 

12 world.

13 And with my last thirty seconds let me 

14 offer one last thought about the role of the 

15 Board and the multi-varied nature of privacy. 

16 Because I think that privacy is many 

17 things and has many applications in many 

18 different contexts, I also think that the most 

19 appropriate ground for making judgements about 

20 privacy is not in boards or judiciaries, but in 

21 the most representative bodies that we have 

22 available to us, in this instance Congress. 
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1 I realize that's perhaps leaning rather 

2 heavily on a body that is not held in the highest 

3 regard at this time, but nonetheless, that is the 

4 mechanism in a democracy for accumulating diverse 

5 preferences, weighing them in the balance and 

6 reaching a judgment for a broader societal 

7 interest.

8 MS. WALD: Okay.

9 MR. ROSENZWEIG: Thank you. My 

10 apologies.

11 MS. WALD: Ed Felten is a Professor of 

12 Computer Science and Public Affairs at Princeton 

13 and founder of the Princeton Center for 

14 Information Technology Policy. So he'll give us 

15 a somewhat different lens through which to view 

16 privacy.

17 MR. FELTEN: Thanks for the opportunity 

18 to testify. Today I'd like to offer a 

19 perspective as a computer scientist on changing 

20 data practices and how they've affected how we 

21 think about privacy.

22 We can think of today's data practices 
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1 in terms of a three stage pipeline. First, 

2 collect data, second, merge data items, and 

3 third, analyze the data to infer facts about 

4 people. 

5 The first stage is collection. In our 

6 daily lives we disclose information directly to 

7 people and organizations. But even when we're 

8 not disclosing information explicitly, more and 

9 more of what we do online and off is recorded. 

10 And online services often attach unique 

11 identifiers to these recordings which are used to 

12 link them up again later. 

13 The second stage of the pipeline merges 

14 the data. If two data files can be determined to 

15 correspond to the same person, for example, 

16 because they both contain the same unique 

17 identifier, then those files can be merged. 

18 And merging can create an avalanche 

19 effect because merged files convey more precise 

20 information about identity and behavior, and that 

21 precision in turn allows further merging. 

22 One file might contain detailed 
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1 information about behavior and another might 

2 contain precise identity information. Merging 

3 those files links behavior and identity together.

4 The third stage of the pipeline uses big 

5 data methods such as predictive analytics to 

6 infer facts about people.

7 One famous example is when the retailer 

8 Target used purchases of a product such as skin 

9 lotion to infer pregnancy.

10 Today's machine learning methods often 

11 enables sensitive information to be inferred from 

12 seemingly less sensitive data.

13 Inferences also can have an avalanche 

14 effect because each inference becomes another 

15 data point to be used in making further 

16 inferences.

17 Predictive analytics are most effective 

18 in inferring status when many positive and 

19 negative examples are available. For example, 

20 Target used many examples of both pregnant and 

21 non-pregnant women to build its predictive model.

22 By contrast, a predictive model that 
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1 tried to identify terrorists from everyday 

2 behavioral data would expect much less success 

3 because there are very few examples of known 

4 terrorists in the U.S. population. 

5 With that technical background let me 

6 discuss a few implications for privacy. First, 

7 the consequences of collecting a data item can be 

8 very difficult to predict. Even if an item on 

9 its face doesn't seem to convey identifying 

10 information, and even if the contents seem 

11 harmless in isolation, the collection could have 

12 substantial downstream effects.

13 We have to account for the mosaic 

14 effect, in which isolated, seemingly unremarkable 

15 data items combine to paint a vivid and specific 

16 picture. Indeed, one of the main lessons of 

17 recent technical scholarship on privacy is the 

18 power of the mosaic effect. 

19 To understand what follows from 

20 collecting an item we have to think about how 

21 that item can be merged with other available 

22 data, and how the merged data can in turn be used 
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1 to infer information about people. We have to 

2 take into account the avalanche effects that can 

3 occur both in merging and inference. 

4 For example, the information that the 

5 holder of a certain loyalty card account number 

6 purchased skin lotion on a certain date might 

7 turn out to be the key fact that unlocks an 

8 inference that a particular identifiable woman is 

9 pregnant.

10 Similarly, phone call metadata, when 

11 collected and analyzed in large volume has been 

12 shown to enable predictions about social status, 

13 affiliation, employment, health and personality.

14 The second implication is that data 

15 handling systems have gotten much more 

16 complicated, especially in the merging and 

17 analysis phases, that is the phases after 

18 collection.

19 The sheer complexity of these systems 

20 makes it very difficult to understand, to predict 

21 and to control how they behave. Even the people 

22 who build and run these systems often fail to 
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1 understand fully how they work in practice, and 

2 this lead to unpleasant surprises, such as 

3 compliance failures or data breaches.

4 Complexity frustrates oversight, it 

5 frustrates compliance and it makes failure more 

6 likely. Despite all best intentions 

7 organizations will often find themselves out of 

8 compliance with their own policies and their own 

9 obligations. Complex systems will often fail to 

10 perform as desired. 

11 Complex rules also make compliance more 

12 difficult. It's sometimes argued that we should 

13 abandon controls on collection and focus only on 

14 regulating use. Limits on use do offer more 

15 flexibility and precision in theory, and 

16 sometimes in practice. 

17 But collection limits have important 

18 advantages, too. For example, it's easier to 

19 comply with a rule that limits collection than 

20 one that allows collection and then puts 

21 elaborate limits on usage afterward. And 

22 collection limits make oversight and enforcement 
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1 easier. 

2 Limiting collection can also nudge 

3 agencies to develop innovative approaches that 

4 meet their analytic needs, while collecting less 

5 information.

6 The third implication is the synergy 

7 between commercial and government data practices. 

8 As an example, commercial entities put 

9 unique identifiers into most website accesses. 

10 An eavesdropper collecting traffic can use these 

11 identifiers to link a user's activity across 

12 different times and different online sites, and 

13 an eavesdropper can connect those activities to 

14 identifying information.

15 Our research shows that even if the user 

16 switches locations and devices, as many users do, 

17 an eavesdropper exploiting commercially placed 

18 identifiers can reconstruct 60 to 75 percent of 

19 what a user does online and can usually link that 

20 data to a user's identity.

21 My final point is that technology offers 

22 many options beyond the most obvious 
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1 technological approach of collecting all of the 

2 data, aggregating it in a single large data 

3 center and then analyzing it later.

4 And here I think Paul's analogy to the 

5 1973 Thunderbird is a good one. We would no 

6 longer accept the safety technologies that were 

7 available on that vehicle. Nowadays we expect 

8 airbags, we expect anti-lock brakes, we expect 

9 crumple zones. We expect the latest technology 

10 to be used to make the technology safer and to 

11 reduce risk. 

12 And we should ask for the same when it 

13 comes to privacy. We should ask agencies to use 

14 advanced technologies to limit how much 

15 information they collect, to use cryptography to 

16 limit undesirable flows of information. 

17 There's a large and growing literature 

18 on privacy-preserving data analysis and methods. 

19 Determining whether collection of particular data 

20 is truly necessary, whether data retention is 

21 truly needed and what can be inferred from a 

22 particular analysis, these are deeply technical 
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1 questions. 

2 In the same way that the Board asks 

3 probing legal and policy questions of the 

4 agencies you oversee, I hope you'll build a 

5 capacity to ask equally probing technical 

6 questions. 

7 Legal and policy oversight are most 

8 effective when they're combined with 

9 sophisticated and accurate technical analysis, 

10 and many independent technical experts and groups 

11 are able and willing to help you build this 

12 capacity. 

13 Thank you for your time and I look 

14 forward to your questions.

15 MS. WALD: Thank you. Okay, for the 

16 next 20 minutes or so I'm going to pose some 

17 questions to the members of the panel, and I'll 

18 pose them to a particular member, but then if one 

19 of the other members has something very cogent, 

20 as I'm sure everything you say is cogent, feel 

21 free to contribute. 

22 Liza, I'm going to start with you. Our 
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1 Constitution defines certain aspects of privacy 

2 in the Fourth Amendment, security of one's home 

3 and papers from unreasonable search and seizure 

4 and protection from general warrants.

5 But are there other aspects of privacy 

6 that the advocacy community believes deserve 

7 legal recognition and judicial oversight, or can 

8 they all be encompassed within the bounds of the 

9 constitutional guarantees? 

10 And if so, what are the ones you think 

11 ought to be specifically recognized, protected in 

12 our law? 

13 MS. GOITEIN: Sure. Okay, so to start 

14 with I suppose the obvious, the Fourth Amendment 

15 applies only to the government. It's a 

16 restriction on the government. It's not a 

17 restriction on private parties. 

18 And I think there's absolutely a place 

19 for regulation of private entities and how they 

20 control, acquire and control people's 

21 information. Because the market doesn't always 

22 do a great job of many things, although it does a 
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1 great job of other things. 

2 But we certainly know that people are 

3 not a hundred percent satisfied with the privacy 

4 protections that have been provided in the 

5 private sector, and that obviously falls outside 

6 of the Fourth Amendment, but is deserving of 

7 regulation.

8 MS. WALD: While I've got you there 

9 before you go on, there was another question that 

10 follows directly from this. 

11 We hear an awful lot about the 

12 commercial acquisition of so much personal 

13 information and what they do with it. And in 

14 fact, the argument is sometimes made, look, don't 

15 worry so much about the government, but some of 

16 the private, Google, some of the communications, 

17 the Internet, have great masses of data. 

18 Do you think that there's any 

19 significant difference in the risks to privacy 

20 that are displayed by the holdings of so much 

21 personal information by the government, as 

22 opposed to private entities, or is it like two 
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1 big behemoths? 

2 MS. GOITEIN: I do think there's a 

3 difference. I think that difference may be 

4 getting smaller, but I think there is a 

5 difference. There remains a difference, which is 

6 that private companies do not have the same 

7 coercive power over the individual that the 

8 government has, and private companies and private 

9 entities don't have the same motivations to 

10 persecute people based on ideology or religion. 

11 I mean these are things that we have 

12 seen in the history of this country, 

13 unfortunately. We have seen people targeted for 

14 surveillance because they were political enemies 

15 of the reigning administration.

16 So what I would say is that private 

17 entities have neither the ability nor the motive 

18 to throw people in jail on pretext because they 

19 are politically opposed to the current 

20 administration. 

21 That said, I think companies, the line 

22 between big companies in this country and 
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1 governments is getting thinner and thinner. And, 

2 you know, certainly companies might have some 

3 political axes to grind with respect to the 

4 workforce, and they certainly have access to 

5 people's information.

6 I am not in the least bit unconcerned 

7 with the private accumulation of information, but 

8 I remain more concerned with privacy vis-a-vis 

9 the government.

10 MS. WALD: Okay. Let me try Professor 

11 Solove. 

12 Now you wrote something in an article 

13 called, Conceptualizing Privacy, and you went 

14 into it a little bit in your prior remarks, there 

15 are sixteen kinds of activities that represent 

16 privacy risks. Privacy itself has six aspects. 

17 They're all defined too broadly and they're all 

18 defined too narrowly. 

19 And so you concluded, I think, if I read 

20 it correctly, that we should concentrate on 

21 specific types of disruptions to those interests 

22 and what should be done about that. 
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1 Can you apply that kind of framework to 

2 the kinds of protection that we need in national 

3 security data and surveillance programs, in 

4 collection processing, identification, secondary 

5 use, all of the other things that you talked 

6 about in your article?

7 MR. SOLOVE: Yes, actually in what I 

8 wrote I talked about privacy not being just one 

9 thing and having a common denominator, but being 

10 a pool of common characteristics and actually 

11 applying to what I laid out was a taxonomy of 

12 privacy about various types of problems. 

13 And I wanted to focus on the problems or 

14 areas where certain activities cause disruption, 

15 they cause problems. And we want to mitigate 

16 those problems. 

17 And what are those problems? Because 

18 that's where we want to step in and say, hey, we 

19 should regulate this, we should do something 

20 about this, we should address these problems. 

21 It doesn't mean that the activities that 

22 cause them are bad, but it does mean that they do 
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1 cause the problems we need to address.

2 Some of these problems that relate to 

3 government data gathering include, one is 

4 aggregation, that you can take a lot of different 

5 pieces of data, each one being particularly 

6 innocuous, not really saying a whole lot about 

7 somebody, but when you combine them together, you 

8 can learn new facts about somebody. This is what 

9 data mining is all about, and data analytics. 

10 The whole becomes greater than the 

11 parts. It starts to create a mosaic, a portrait 

12 of somebody. 

13 This then leads to the revelation of 

14 information that someone might not have expected 

15 or wanted when they gave out little pieces of 

16 information here and there. 

17 And I think this causes a problem. It 

18 disrupts people's privacy expectations. It can 

19 lead to knowledge of information that people 

20 don't want exposed or that society might not want 

21 exposed. And so I think we need to address that 

22 problem. 
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1 And oftentimes conceptions of privacy 

2 will ignore aggregation because they'll say, 

3 well, if the information all were different facts 

4 that were gathered from public information 

5 there's no privacy. 

6 But I don't think that's true. I think 

7 we really want to look at what the problems are 

8 and if we look at the problems, there's a problem 

9 here.

10 Another aspect is a problem I call 

11 exclusion, which is the fact people lack an 

12 ability in a lot of cases to have any say in how 

13 that information might be used against them, any 

14 right to correct that information, or to make 

15 sure that it's accurate. 

16 And I think that's a key component of a 

17 lot of privacy laws is there's a right for people 

18 to make sure that proper decisions are being made 

19 about them based on their information. 

20 I can't go through all sixteen. I can 

21 hit some others. 

22 One is identification, the fact that 
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1 this involves linking a body of data, what I call 

2 a digital dossier to a particular individual. By 

3 identifying them you actually are connecting them 

4 to data that then can be used to make decisions 

5 about their lives. Some of the decisions could 

6 be good, but some decisions could in fact be 

7 harmful to an individual. 

8 Security is another issue that I see as 

9 related and part of my taxonomy of privacy, and 

10 that's keeping data secure. When data isn't kept 

11 secure, it creates risks and vulnerabilities to 

12 people that could expose them to a lot of harm 

13 if, in fact, the data is leaked improperly. 

14 And that happens all the time. We're 

15 all at risk when all this data is gathered 

16 together in a big repository. 

17 There are a lot of other things, but 

18 I'll stop here in the interests of time. 

19 But these are just some of the ways that 

20 the taxonomy addresses this problem. I think 

21 it's important to think of the overarching point 

22 is don't start with some platonic concept of 
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1 privacy and see, you know, what fits in it and 

2 what doesn't.

3 I think it's better to look at things 

4 from the bottom up and say, where are the 

5 problems here? What are the problems and harms 

6 that are caused by these activities and how do we 

7 address those harms? 

8 MR. ROSENZWEIG: Could I just make a 

9 brief comment? 

10 MS. WALD: Yes, sure.

11 MR. ROSENZWEIG: I would agree with 

12 everything that Dan said but I would also say 

13 also look at what the benefits are. 

14 You know, the President's report on big 

15 data looked at the increase in the volume, 

16 velocity and variety of data, and championed the 

17 idea that large scale data aggregation creates 

18 ubiquitous new knowledge -- serendipitous new 

19 knowledge that is of value to society as well. 

20 So it brings with it harm, but it also 

21 brings with it benefits, and that is why I see it 

22 as a kind of cost benefit utilitarian analysis. 
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1 MS. GOITEIN: Sorry.

2 MS. WALD: Yes, go ahead. 

3 MS. GOITEIN: I just want to say one 

4 thing quickly. I thought this it's a utilitarian 

5 value, not a human right, it is a human right. 

6 It's listed in the Universal Declaration of Human 

7 Rights, it's listed in the ICCPR, and other 

8 treaties and protocols that the United States has 

9 signed and that have the force of customary 

10 international law. 

11 So whatever one's personal feelings 

12 about that, I don't think this Board has the 

13 latitude to decide that all these treaties we've 

14 signed declaring it as a human right are void.

15 MS. WALD: Of course defining what's 

16 included in that human right has been one of our 

17 problems, it's been one of your problems, it's 

18 been everybody else's problem. 

19 MS. GOITEIN: Of course, but it's a 

20 human right.

21 MR. SOLOVE: May I make one small point?

22 MS. WALD: Yes.
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1 MR. SOLOVE: And that is I think I 

2 totally agree about the benefits of big data and 

3 the use of these things, but I think often the 

4 balance is wrongly cast between, okay, here, 

5 let's take the benefits and let's weigh it against 

6 the harms. 

7 Because protecting privacy doesn't mean 

8 getting rid of big data, or not engaging in 

9 surveillance, or not doing a search. The Fourth 

10 Amendment allows searches and allows 

11 surveillance, for example, it just requires 

12 certain oversight. 

13 So what we need to look at when we're 

14 balancing is not all the benefits of big data 

15 against privacy, we need to look at to what 

16 extent do oversight, accountability and these 

17 protections on it, to what extent do they 

18 diminish some of those benefits. 

19 And that difference, that diminishment 

20 is what gets put on the scale against privacy, 

21 not all of big data's benefits. And I think if 

22 we weigh that appropriately, then I think we get 
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1 a better balance.

2 MS. WALD: All right. 

3 MS. GOITEIN: Very quickly.

4 MS. WALD: Briefly. 

5 MS. GOITEIN: Yes. We don't get to 

6 weigh these things de novo when it comes to the 

7 Fourth Amendment. The balance has been struck. 

8 The government can't say we want to do searches 

9 in people's houses, we have a really good reason, 

10 we don't have a warrant, but we have a really 

11 good reason, let's everybody do this balance 

12 anew. 

13 That balance was struck by the drafters 

14 of the Fourth Amendment. You need, in the vast 

15 majority of cases, there are some narrow, 

16 delineated exceptions, but you need a warrant 

17 based on probable cause of criminal activity to 

18 do those searches. This is not starting from 

19 scratch.

20 MS. WALD: Mr. Rosenzweig, your 

21 approach, and you talked a little bit about this, 

22 your approach for balancing privacy and national 
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1 security has I think been termed, whether you 

2 call it instrumental or consequential, but in one 

3 of your articles you talked about you thought 

4 limiting the right of somebody to complain or to 

5 go to court, etcetera, to intervening on the 

6 basis of when they are suffering a tangible harm, 

7 like a warrant or being called before the grand 

8 jury, as opposed to Professor Solove's views of 

9 privacy as a kind of foundational value, 

10 recognizable in its own right. 

11 Yet you also recognize in some of your 

12 other works the significance of some aspects of 

13 privacy to a democratic society. 

14 Now all of you have talked about it 

15 isn't just an individual right, it's a right that 

16 an open society needs, starting with even the 

17 necessity for people developing their 

18 personalities in an atmosphere in which they feel 

19 free to experiment a little bit, to have 

20 relationships, to talk without feeling that 

21 they're constantly being judged by the government 

22 or society. 
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1 I'm wondering how you reconcile your 

2 recognition of the aspects of privacy that are 

3 necessary to a democratic open society with this 

4 notion that we really shouldn't start intervening 

5 until somebody is suffering some tangible harm.

6 MR. ROSENZWEIG: Well, thank you for the 

7 question. I think that I don't see them as 

8 irreconcilable because I see the question about 

9 the adversity of consequence and the error 

10 correction mechanisms as critical to the first 

11 part of your question, the inherency of the 

12 value. 

13 To say that, I sort of sometimes use a 

14 thought experiment, which is, what if in some 

15 hypothetical world, which I assure you does not 

16 exist, the government never abused anybody, never 

17 actually misused the data it was collecting, 

18 never, had no lists of enemies, no persecution, 

19 and never made a mistake. 

20 Now granted, that's an impossible 

21 standard, but if that were the instance then in 

22 the long run the values that underlie, the 
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1 democratic values, would be supported and people 

2 would no longer fear the collection because the 

3 lack of adverse consequence, or by hypothesis, a 

4 hundred percent would have gone away. 

5 So to my mind, the way to support the 

6 values that we see in the underlying democratic 

7 sphere is to build the error correction 

8 mechanisms, the audits, the oversights, this 

9 Board, into the process in a way that reassures 

10 society, as much as possible, that we're driving 

11 down the errors, and frankly both types, the 

12 false positives and the false negatives, but this 

13 Board is principally concerned with the false 

14 positives, driving down the errors as much as we 

15 humanly can.

16 We don't eliminate government programs 

17 because of the possibility of error because every 

18 government program, every human endeavor has the 

19 possibility of error. 

20 We arm police officers, even though we 

21 know that they will sometimes misuse their 

22 weapons. We don't eliminate that. We try and 
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1 drive down the error rate as much as possible so 

2 that we engender people's confidence in the 

3 police. 

4 And we see sadly these days exactly what 

5 happens when people's confidence in the police is 

6 not maintained, that our error correction 

7 mechanisms are deemed by society inadequate.

8 And I think we're sort of seeing some of 

9 the same thing in response to the Snowden 

10 disclosures as well. But that suggests to me 

11 that the right way to support the underlying 

12 values is to go back and think about how to fix 

13 the error correction mechanisms.

14 MS. WALD: So let me just pursue one 

15 thing that you brought up earlier, which has come 

16 up in some of our past reports and is bound to 

17 come up in future ones, I think, and that is at 

18 what stages, if you would go into a little bit 

19 more into the point at which you think an 

20 independent review of decisions outside of the 

21 government's internal auditing and processes are 

22 necessary to ensure that you have this kind of 
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1 trust by the people that the government is not 

2 taking risks to its privacy, and in terms of what 

3 you yourself suggested that history has got some 

4 lessons for us on the "trust us" aspect.

5 MR. ROSENZWEIG: Well, I certainly don't 

6 dispute that we've had failures in the past. 

7 Anybody who would dispute that hasn't read 

8 history. 

9 I would say that there's no one size fit 

10 all answer, that it really depends upon the harms 

11 involved and the nature of what you anticipate 

12 the failure mode would be. 

13 I'll give you two examples. On the one 

14 side we have the current TSA inspections programs 

15 at the airport. Probably a fairly significant 

16 error rate of false positives, pulling people 

17 aside for secondary inspection.

18 On the other hand, a comparatively 

19 modest intrusion. And I say that knowing that 

20 many people think it's a very large intrusion, 

21 but nonetheless comparatively modest compared to 

22 the coercive nature of being put in jail, for 
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1 example. 

2 So in that instance we seem reasonably 

3 happy with a principally administrative 

4 methodology that doesn't require any outside 

5 check because individual liberty is not at issue, 

6 long-term confinement is not at issue. The 

7 degree of harm is small. 

8 By contrast you will infer that I 

9 certainly think that independent review is 

10 essential whenever people's liberty is at stake, 

11 when significant aspects of livelihood are at 

12 stake. 

13 I think that one of the strangest things 

14 that I see in the privacy debates today is that 

15 we seem to get all wrapped up about things like 

16 the TSA screening and we don't look at how 

17 government databases are used to deny employment 

18 to people. 

19 You can't get a job in the 

20 transportation industry with a record, even if 

21 the record is itself ripe with error, because of 

22 the transportation worker identity card.
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1 So we, I think, have it backwards a 

2 little. And that would be an instance where an 

3 independent review of some sort for somebody 

4 who's denied employment in the nuclear industry 

5 or in the transportation industry would be right.

6 So putting that in this context I 

7 certainly think that any time there is an adverse 

8 consequence to an individual that we get to the 

9 point where there is room for a judicial 

10 intervention, an independent intervention.

11 That's why I sort of like what the 

12 President has done in adding the reasonable 

13 articulable suspicion trigger to the querying of 

14 the 215 database because that's the point at 

15 which some individual becomes, you know, out of 

16 the mass pulled out for individuated scrutiny, 

17 and that's the point at which he begins to at 

18 least suffer the risk of inaccurate adverse 

19 consequences. And so I sort of like that as a 

20 transition point.

21 MS. WALD: Good.

22 Mr. Felten, you talked about the 
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1 tendency for institutions, including the 

2 government, to build ever larger databases and 

3 then to aggregate them. And I think you've said 

4 either today or in some of your writings that 

5 there are inherent risks to privacy interests 

6 when the databases get larger and larger, and 

7 especially when they aggregate them. 

8 So I guess my basic question to you is, 

9 what are the principles, and we'll all take notes 

10 on this, what are the principles that you 

11 recommend as a computer expert for protecting 

12 privacy in the increasing use of technology in 

13 this field? 

14 I mean all along the way from 

15 collection, aggregation, whatever you think, if 

16 somebody wanted to design a system in broad 

17 concepts that maximize privacy but took adequate 

18 concern for security, what would they look to? 

19 MR. FELTEN: Sure. Well, I think the 

20 first principle would be to try to look beyond 

21 the most brute force technological approach, 

22 which is collect all the data that might turn out 
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1 to be useful and retain it all in a single large 

2 data center for as long as you can. 

3 The more data you have, the more you 

4 collect, the greater the adverse consequences 

5 could be, the greater the risk and the more of a 

6 target it is, either for abuse or for breach. 

7 So the first principle is to try to fit 

8 the practices to the specific, to think in terms 

9 of what kind of analysis it is that you know you 

10 need to do and figure out which data you can 

11 collect and how you can structure a system in a 

12 way that can do that analysis, while collecting 

13 less data, holding the data more separately and 

14 pre-processing or minimizing the data first. 

15 And there's a growing array of technical 

16 methods that can do this. And unfortunately, 

17 this becomes a technical problem. 

18 So the key principle here is simply to 

19 insist that that technical work be done to try to 

20 architect a system to collect and hold a minimum 

21 of data.

22 MS. WALD: Who should do that, the 
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1 government or private industry? 

2 MR. FELTEN: In my view if, say, a 

3 government agency wants to argue that they have a 

4 need to collect and use certain data, there 

5 should be some onus on them to justify the 

6 technical practices they're using to justify the 

7 amount of data collected, the way they're 

8 organizing it and so on. 

9 That those who would argue for a 

10 collection and use of data should be prepared to 

11 discuss these issues and offer a technical 

12 justification. 

13 When it comes to private parties that's 

14 a more complicated discussion. I think that the 

15 best practice in industry ought to be to do that 

16 as well, although obviously the legal and market 

17 mechanisms that drive that relationship are very 

18 different.

19 MS. WALD: My last question I'm going to 

20 throw out, and you can all take a whack of it if 

21 you want to, but several of you, and I think you 

22 especially, Ms. Goitein, have talked about the 
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1 element of control of information as being 

2 essential, but then some other people have 

3 written in the field said, well, that certainly 

4 can't be an absolute value. There's got to be 

5 balance. I mean we wouldn't be able to have the 

6 kind of national security programs if indeed 

7 everybody said, well, I'm keeping control over 

8 that piece of information because I don't want 

9 anybody to have it, etcetera. 

10 So what kinds of principles do you 

11 apply? Because I assume you recognize that some 

12 balancing, even as Paul pointed out that even in 

13 the Fourth Amendment there's an unreasonable 

14 clause which gives you a kind of a balancing to 

15 talk about, so how would you handle that? 

16 And then everybody can take a whack at 

17 it, and then my panelists will take some more 

18 whacks. 

19 MS. GOITEIN: So as I said before, I 

20 think in the vast majority of circumstances, and 

21 that's the way it should be anyway, the drafters 

22 of the Fourth Amendment did that balancing for us 
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1 and gave us what the government had to do to 

2 override the privacy right, and that is to show 

3 probable cause of criminal activity.

4 There are some very narrow exceptions 

5 that the Supreme Court has recognized, some of 

6 which are controversial, some of which are not. 

7 And again, so we're not starting from 

8 scratch. We have to follow the Supreme Court 

9 case law here. We can't just say, well, I think 

10 this particular search was reasonable, even 

11 though there wasn't a warrant. If it doesn't 

12 fall within one of the delineated exceptions for 

13 the court, you have to get a warrant based on 

14 probable cause. 

15 Within those exceptions there is for 

16 balancing, and that's part of the reasonableness 

17 analysis. What I would say about that is, first 

18 of all, the courts do their balancing when they 

19 do a review. 

20 But Congress has a role as well. And 

21 when Congress does the balancing on behalf of the 

22 people, I would agree with what I believe Dan 
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1 said, which is that this is a choice for the 

2 public to make. This needs to be a public choice 

3 and it needs to be an informed choice, not a 

4 choice that's made in secret by a small number of 

5 officials, but by the public, because this is a 

6 democracy. 

7 So we need to have the information about 

8 what the security is, how that threat could be 

9 mitigated by the collection of this information, 

10 and what exactly is going to be the effect on 

11 either side. 

12 The other quick point I would make is 

13 that in balancing tests national security is too 

14 often a trump card. The words are uttered and 

15 we're done. 

16 And Julian Sanchez from the Cato 

17 Institute made an excellent point, which is when 

18 you look at how courts weigh national security 

19 against the individual interests in question, 

20 they tend to weigh national security writ large 

21 over that person's particular interest in that 

22 information. And that's not the right 
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1 comparison. You either need to weigh that 

2 person's particular interest in that particular 

3 information against the incremental threat to 

4 national security in that case, or you need to 

5 address national security writ large, weigh 

6 national security writ large against the values 

7 that privacy serves in our society.

8 And when you think of it that way, 

9 national security really shouldn't be a trump 

10 card. 

11 You know, we talk about these values as 

12 being in competition. I think the evidence for 

13 the most part shows that targeted surveillance is 

14 more effective than dragnet surveillance. But 

15 when they are in conflict there needs to be a 

16 fair and public balancing.

17 MS. WALD: Okay, thank you. Some other 

18 comments? I'll let everybody have a shot at this 

19 so we can go down the line. We'll start at that 

20 end with you.

21 MR. FELTEN: Okay, thanks. I think in 

22 thinking about these issues of control, it's 
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1 important to recognize the ways in which people 

2 try to reassert control, even if they don't have 

3 it legally. 

4 And I'm referring specifically to 

5 technical self-help measures that people use to 

6 try to limit the flows of information, to try to 

7 obfuscate identify and behavior, as well as 

8 strategic behavior in which people avoid doing 

9 certain things or deliberately do certain things 

10 in order to present a different kind of image to 

11 whoever it is that they worry is looking at their 

12 data.

13 And these things have substantial costs. 

14 And I think if you're trying to do a kind of 

15 utilitarian balancing like Paul was talking 

16 about, you need to take into account the ways in 

17 which resources are spent and sometimes are 

18 really wasted in a kind of arms race between 

19 self-help and strategic behavior on the one hand, 

20 and attempts to overcome that on the other side. 

21 And those costs can often be 

22 substantial. Just ask any teenager about their 
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1 online use and what you'll hear, and privacy, and 

2 what you'll hear is an elaborate story about 

3 technical countermeasures and strategic behavior.

4 MS. WALD: Paul?

5 MR. ROSENZWEIG: I think your point is 

6 generally well taken, which is to say that 

7 fundamentally the notion of control is at odds 

8 with government collection of information, 

9 whether it's for the purpose of imposing a tax 

10 under the IRS, or law enforcement, or national 

11 security. 

12 That doesn't mean that it's not an 

13 important value. It is one that many would 

14 advance, and I see no reason to discount that at 

15 all. 

16 But in some ways if you advance that as 

17 the touchstone of what you mean by privacy, 

18 you're setting privacy ineluctably in opposition 

19 to effective government action in a host of areas 

20 where people might reasonably want to control.

21 You know, I'm sitting here as a 

22 Republican on the panel thinking of all the 
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1 friends I have who are Second Amendment people 

2 who think that the government should not collect 

3 any information about their gun ownership. And, 

4 you know, that's a perfectly reasonable position 

5 for them to have. It's not one that we currently 

6 accept as society.

7 And then the last point I would make, 

8 which is just in response to Liza, because she's 

9 mentioned it twice, but when I was last in 

10 government the percentage of searches that were 

11 conducted without warrants was actually quite 

12 high, on the order of 50 percent. 

13 Now I don't know if that's changed much 

14 because it's been a while since I've been a 

15 prosecutor, but many, if not most of our typical 

16 interactions with law enforcement are adjudicated 

17 on an ex-post reasonableness standard rather than 

18 an ex-ante warrant standard. 

19 I don't actually have the data so I 

20 can't say more about it than that, but I 

21 certainly seem to recall that it's not always a 

22 pre, as opposed to a post activity, judicial 
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1 review.

2 MS. WALD: Dan, you have the last word.

3 MR. SOLOVE: Sure. A few really quick 

4 points. First of all, even if you can't always 

5 give people a total control, there are certain 

6 partial things that we can give people for 

7 control. 

8 And the other thing is that it's not 

9 just people being in control, it's that the uses 

10 and gathering of the information is under 

11 control. And that's another important thing 

12 about it, that there's appropriate oversight and 

13 accountability and controls on that gathering 

14 too. 

15 On the Fourth Amendment, I think that it 

16 would be wrong just to track existing Supreme 

17 Court interpretations of the Fourth Amendment, 

18 which I think are a lot of times flawed in a lot 

19 of cases. 

20 In fact, I think there are a lot of 

21 exceptions to the warrant requirement, a lot of 

22 instances where the Fourth Amendment doesn't even 
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1 get applied at all because the court has this 

2 platonic conception of privacy that is incredibly 

3 narrow. 

4 And that's how we get the third-party 

5 doctrine and how we get a lot of bodies of Fourth 

6 Amendment law that often take the Fourth 

7 Amendment away from any kind of approach. 

8 Now the Fourth Amendment, I think, is a 

9 utilitarian balancing. It says basically the 

10 right to be secure against unreasonable searches 

11 and seizures. It actually doesn't say privacy, 

12 it says actually a right to be secure against 

13 unreasonable searches and seizures. 

14 And I think that means that any time the 

15 government is engaging in searches, and 

16 surveillance, and gathering information that it 

17 is unreasonable if it's creating problems that 

18 are not adequately dealt with the right amount of 

19 oversight and accountability. 

20 And that's really what the Fourth 

21 Amendment is trying to impose there, either 

22 justification to gather information, such as a 
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1 warrant and probable cause, or appropriate 

2 oversight to make sure that an independent 

3 judicial body looks at what the government wants 

4 to do and evaluates it.

5 I think it's very important that we 

6 conduct the balance between privacy and security 

7 appropriately. 

8 I'm not a privacy absolutist, I think 

9 that there should be a balance. But I think it's 

10 very important that when we balance, we balance 

11 it correctly and not incorrectly, and that we 

12 don't skew the balance too much to the security 

13 side by overweighing the security interests, 

14 because it's not the entire security interests on 

15 the scale. 

16 It's the marginal difference between a 

17 security interest without certain kinds of 

18 oversight and accountability, and the security 

19 interests with oversight and accountability.

20 And I think all branches should be 

21 playing a role and have a role to play in this. 

22 Congress in the 1970s at the Church Committee, 
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1 which did an extensive review of intelligence 

2 agencies, produced a very illuminative public 

3 report about that.

4 Congress hasn't done anything quite like 

5 that since. I think it should. 

6 I think the judiciary has a role to 

7 play. I think this body has a role to play. I 

8 think the people ultimately also are the key to 

9 all this. They have a role to play.

10 MS. WALD: Thank you. We're now going 

11 to have 20 minutes of questioning from my fellow 

12 Board members, and I think I'll start with the 

13 Chair.

14 MR. MEDINE: Great, thank you. 

15 Liza raised a question about the proper 

16 standard for privacy and referenced the Katz 

17 decision essentially, on expectation of privacy 

18 and how in some ways people rely on practical 

19 obscurity because the government is too complex 

20 or burdensome to gather information.

21 In some ways in the computer age we're 

22 beyond that, which is that the court file that 
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1 was gathering dust now is easily accessible and 

2 public. 

3 The question is, how should we look at 

4 privacy issues when public databases are so 

5 readily available? 

6 And there's also a reference to the fact 

7 that the line between government and commercial 

8 databases isn't always great and the government 

9 can access commercial databases. 

10 So how do we look at privacy when the 

11 information is out there, it is publicly 

12 available, but yet, as Ed pointed out, you 

13 combine it into a mosaic and it can create a very 

14 detailed profile, and should the government be 

15 collecting that information? 

16 So what standard should we apply in this 

17 context? What's the Katz 2014 version as far as 

18 how the government ought to recognize privacy 

19 issues?

20 And I'm happy just to go down the line.

21 MS. WALD: Well, whoever wants to take 

22 it. I might note that time-wise, we're going to 
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1 have about five minutes per, so if you can keep 

2 your answers or your comments relatively brief, 

3 we can make sure that everybody gets their full 

4 component of time.

5 MR. SOLOVE: I'll be super-brief. I 

6 think that right now what's been known as the 

7 mosaic theory that we see in the concurrences to 

8 the Jones case in the Supreme Court are starting 

9 to look at this very question. 

10 I can't really answer it in a few 

11 seconds, but I think it's to look at when we 

12 combine various pieces of data, what are the 

13 implications of that and when does the combining 

14 of that data reveal new information that can 

15 create certain problems and harms to people, and 

16 that's where we want to step in.

17 MR. ROSENZWEIG: I'd make two quick 

18 points. The first is of course that practical 

19 obscurity is itself a sort of a post-industrial 

20 concept. 

21 If you were in a medieval village back 

22 in the 1200s there was no practical obscurity. 
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1 You were limited to who you knew and they knew 

2 everything about you, pretty much. The data 

3 aggregation system was the coffee klatch where 

4 everybody talked to each other. 

5 So in advancing practical obscurity 

6 we're advancing a value that has come to be 

7 something that we value more now, one that I 

8 agree with. 

9 I think Dan's exactly right, the mosaic 

10 is real. To deny that is to deny the reality of 

11 the science that Ed knows. 

12 So it strikes me that the most likely 

13 points of intervention are either at the 

14 collection, or at the aggregation, or at the use 

15 of the aggregated data. 

16 I tend to think you can't do it at 

17 collection because the databases are there. 

18 They're so big it's impossible to stop. Unless 

19 you're going to stop Google from collecting, 

20 we're going to have big data collection. 

21 So it's got to be when the government 

22 chooses to aggregate it or perhaps chooses to act 
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1 upon the aggregation. And as between those, I 

2 don't have too much choice. 

3 MS. GOITEIN: Just a quick note, I agree 

4 with most of what's been said in terms of the 

5 mosaic theory. 

6 I mean another way to look at it is just 

7 that the information that's being gathered by the 

8 government is, in fact, information that using 

9 normal powers of human observation would be in a 

10 person's control and would not be something the 

11 government would have access to. 

12 The one thing I would say is that I 

13 don't agree that the point of collection is a 

14 moot point because the mere fact that Google has 

15 all of this information and Facebook has all of 

16 this information does not mean that the 

17 government has all of this information. 

18 And there are burgeoning new 

19 technologies that their use has not been decided, 

20 such as UAVs and how the government will be 

21 allowed to deploy UAVs. So there's still plenty 

22 of room to regulate at the collection phase. 
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1 And for all the reasons we discussed 

2 earlier about chilling effect and about what 

3 privacy means to different people, I think that 

4 is the point at which the privacy interests 

5 arise.

6 MR. FELTEN: And I'll be very brief as 

7 well. Along with what the other panelists have 

8 said, I'd also point out that much of the 

9 information that is in corporate databases is 

10 information that was observed rather than 

11 disclosed, and there's not always consent, or at 

12 least often consent is very thin from the person 

13 who the data is about. 

14 And so I don't think you can always 

15 infer that there was an awareness. You can't 

16 infer from the fact that information is in, say, 

17 a corporate database that a user was aware that 

18 it was collected or that they were aware that it 

19 might go to the government and be used for 

20 government purposes.

21 MR. MEDINE: And therefore should the 

22 government not collect the information under 
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1 those circumstances? 

2 MR. FELTEN: Well, I hesitate to make a 

3 legal opinion here, not being a lawyer.

4 MR. MEDINE: As a policy matter.

5 MR. FELTEN: As a policy matter?

6 MR. MEDINE: Yes.

7 MR. FELTEN: But I should say that as a 

8 policy matter I get very nervous when it appears 

9 that there is a legal fiction that something has 

10 happened when it's clearly not happening. So a 

11 fiction of consent or a fiction that the mosaic 

12 effect doesn't exist are troubling.

13 MR. MEDINE: My time has expired.

14 MS. WALD: Rachel Brand.

15 MS. BRAND: Thank you. Thank you all 

16 for being here, first of all. 

17 Going back to this notion of control 

18 that Judge Wald was asking about, Ms. Goitein, 

19 you went to the Fourth Amendment concept. I'm 

20 interested in whether the notion of control 

21 that's embodied in the FIPPs, which is more of an 

22 individual participation concept, can apply in 
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1 the national security surveillance context. 

2 So I think one of you noted that maybe 

3 no individual would say, yes, I consent to being 

4 surveilled by the NSA, or the FBI, or anybody 

5 else, and if that were the standard then you 

6 couldn't have surveillance programs. 

7 And the FIPPs is on top obviously of 

8 whatever the Fourth Amendment baseline is. FIPPs 

9 would impose on government agencies additional 

10 restrictions. 

11 Can that kind of notion apply at all in 

12 the national security context? What's your view 

13 on that? 

14 MS. GOITEIN: I think it can apply but 

15 I'm just sort of pausing because I'm thinking 

16 about some of the premises of the questions.

17 It's not the case that you couldn't have 

18 surveillance programs if people didn't consent to 

19 the disclosure of their information. The 

20 government can obtain your information with a 

21 warrant based on probable cause.

22 MS. BRAND: No, no, my point is that 
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1 we're beyond the Fourth Amendment now. We're 

2 layering on top of the Fourth Amendment the FIPPs 

3 individual participation notion. 

4 And the reason I ask is because, for 

5 example, when the NSA published their report on 

6 targeted data collection under 12333, they said 

7 that they were applying the FIPPs, but then they 

8 turned right around and said, but the individual 

9 participation concept doesn't apply so we're not 

10 applying that part of it. 

11 So what I'm wondering is whether the 

12 FIPPs is just not the right, I don't know, 

13 framework to apply in this context. That's what 

14 I'm trying to get at. 

15 Is it, does this individual 

16 participation thing just not apply and should we 

17 look for some other framework or standard to use? 

18 That's what I'm getting at.

19 MS. GOITEIN: Actually if you wouldn't 

20 mind I'd like to think about the question.

21 MS. BRAND: Okay.

22 MS. GOITEIN: I have some thoughts but I 
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1 want to think about it a little more and maybe I 

2 can put it in writing along with my testimony.

3 MS. BRAND: Okay.

4 MR. SOLOVE: I have a thought on it. I 

5 think the FIPPs model has some flaws to it. You 

6 know, a lot of times people don't read the 

7 privacy policies, in most cases, of companies. 

8 And I'm not sure just providing a notice is 

9 effective.

10 So we do need to think about what works 

11 in this context. I think that the key is in 

12 certain cases we might want individuals to play a 

13 greater role. I think the TSA, if you're on the 

14 no-fly list, I think you should have a right to 

15 be heard. There should be rights of redress 

16 there and to challenge your being on that list. 

17 So I think there some of the FIPPs make 

18 a lot of sense. Some of the FIPPs like security 

19 I think make sense. Other ones might not. 

20 But I think the larger component of all 

21 this is that there's adequate control and 

22 accountability, which is also part of the FIPPs. 
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1 So while everything in the FIPPs, such 

2 as an individualized notice of every time 

3 information is collected is not really feasible, 

4 there are certain things. 

5 There's a greater transparency right in 

6 the FIPPs too, not that individuals get notified 

7 about every collection about them, but that 

8 there's a public accountability and a generalized 

9 disclosure about what's going on. 

10 MR. ROSENZWEIG: I thought that the 

11 acknowledgement in the NSA report that some of 

12 the FIPPs principles simply could not be fully 

13 implemented in the context of a national security 

14 surveillance program was an absolutely accurate 

15 acknowledgement of reality. 

16 You can't provide error collection 

17 notice in all circumstances. I certainly agree. 

18 I mean was talking more about the secondary 

19 screening than the no-fly list where we do have 

20 more robust rights. 

21 But the challenge for you is going to be 

22 trying to figure out what the underlying values 
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1 are and how to get at those. 

2 So in this context I think the 

3 underlying value is prevention of governmental 

4 abuse, that's what animates everybody in this 

5 sphere, and government surveillance modifying 

6 behavior. 

7 And the types of accountability and 

8 transparency that you have to help build are ones 

9 that match the operational needs of the national 

10 security system, while providing protections 

11 against that. 

12 We tried that with the intelligence 

13 committees and the post-Church Commission 

14 modifications, something that we might call kind 

15 of delegated transparency where we all trust the 

16 Congress to do it right. 

17 It seems as though we're less willing to 

18 do that now. Personally I'm not so certain that 

19 that's a good impulse, but it seems like that.

20 So maybe it's this Board. Maybe it's a 

21 judicial panel with a cleared advocate in front 

22 of it. 
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1 There are lots of mechanisms, short of 

2 the complete transparency and accountability and 

3 individual participation that are part of FIPPs 

4 that could be imagined that would achieve the 

5 objective of controlling against governmental 

6 abuse and misuse, while not completely 

7 frustrating the operational necessities that I 

8 think most of us see as remaining regnant. 

9 So I think a lot of it would be things 

10 that are more in Ed's bailiwick, which are 

11 thinking about what the use case scenarios that 

12 are legitimate are in advance and building in 

13 enhanced privacy protections on a technological 

14 level. And then you can have as much of your 

15 cake as you want and still get to eat some of it.

16 MR. FELTEN: To the extent that 

17 particular principles from the FIPPs might be 

18 difficult or impossible to apply in this kind of 

19 setting, it seems there should be a greater 

20 obligation to further the goals of that 

21 principle.

22 So, for example, if you can't offer the 
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1 right to control or correct errors in the data, 

2 you could imagine asking for greater effort to 

3 ensure the correctness of the data as it is, or 

4 extra safeguards ex-post regarding the 

5 possibility of error. 

6 MS. BRAND: Thank you.

7 MS. WALD: Okay. Did you collect any 

8 thoughts that you, very briefly -- 

9 MS. GOITEIN: Yes, and I think I would 

10 agree with Ed. I mean part of what I was 

11 struggling with is how much are we giving up on 

12 this sort of collection, which I'm not quite 

13 willing to do, in talking about sort of, you 

14 know, post-collection? And that's why I wanted 

15 to go back to that issue of surveillance and 

16 control over the information. 

17 I still want to go back and look more. 

18 This is honestly something I just haven't thought 

19 about enough and so I do want to go back and, you 

20 know, look at FIPPs, which I used to use all the 

21 time when I was on the Hill to craft our privacy 

22 amendments, but I want to go back and look more 
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1 carefully. 

2 But, you know, it sounds to me like the 

3 best approach is a modified version of the FIPPs, 

4 but I want to look more closely.

5 MS. WALD: That's fine. We'll be glad 

6 to receive any later submissions from any of the 

7 panelists.

8 Before we go on to Beth Cook's questions 

9 I want to remind the audience that if you have 

10 any questions, write them down and they'll bring 

11 them up to me and then I will -- okay, they're 

12 coming, that's good to know.

13 Okay, Ms. Cook.

14 MS. COLLINS COOK: So thank you all for 

15 what I've found to be a very, very interesting 

16 panel. I hope it bodes well for the rest of the 

17 day. 

18 And in fact, a lot of, I think panel 3 

19 will be dealing exactly with how you translate 

20 the FIPPs, is that the right transition, does 

21 that really work in the government context.

22 But I was also struck by the numerous 
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1 mentions of mosaic theory because there are 

2 obviously other implications of the mosaic 

3 theory. 

4 One bears on transparency, which is to 

5 the extent that we are transparent in seemingly 

6 discreet ways, our adversaries are also looking 

7 to aggregate information about sources and 

8 methods. 

9 The other is that the national security 

10 establishment I think would argue that the mosaic 

11 theory is critical. You need to understand 

12 mosaic theory to understand collection, to 

13 understand exactly how the national security 

14 apparatus works, that they have to be able to 

15 aggregate information. 

16 You can agree or disagree, but I think I 

17 was struck by the different implications of the 

18 mosaic theory.

19 So I wanted to start with you, Professor 

20 Felten, and I was really interested in your 

21 notion of moving away from the brute force 

22 collection mechanism. 
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1 And I think the Section 215 program is 

2 one that the government had made the argument 

3 essentially that they need the brute force 

4 collection, they need to have the retention in 

5 order to identify previously unknown links and 

6 information. 

7 Have you given thought as to whether or 

8 not there are technological options available to 

9 limit collection for a program like Section 215? 

10 If you haven't, then more generally if 

11 could you be more specific about collection 

12 options.

13 MR. FELTEN: Yes. Well, with respect to 

14 Section 215, the data of course is collected 

15 initially by the phone companies, right? And 

16 there's the question of whether the information 

17 needs to be transferred in bulk to the 

18 intelligence community in order for them to be 

19 able to do their analysis. 

20 And I think it's pretty clear that as a 

21 technical matter the kinds of linking, looking 

22 for, say, multi-hop links that the intelligence 
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1 agencies want to do, can be done technically 

2 while the information is still held by third 

3 parties such as the phone companies. 

4 This requires a modest amount of 

5 technical coordination between the companies, the 

6 entities holding the data and the entities that 

7 are doing the analysis.

8 So there are opportunities to match, to 

9 look for whether there are paths of two hops or 

10 three hops from point A or point B, etcetera, and 

11 then to reach in and extract just the data items 

12 of those individuals or phone numbers that are 

13 highlighted by that kind of analysis. That's the 

14 kind of thing that can be done. 

15 There's further work that is more 

16 technical that goes to questions of whether you 

17 can use, say, advanced cryptography to allow that 

18 same analysis, while not disclosing to the phone 

19 company information about which numbers are being 

20 searched or linked. 

21 And those sorts of methods are, I'd say 

22 developing, and there's been some interest in the 
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1 technical problem of how to do this in the 

2 independent research community in light of what 

3 we've learned publicly about the Section 215 

4 program. 

5 And one of the lessons of that is that 

6 methods are often available or developable when 

7 you have a specific technical problem like this.

8 MS. COLLINS COOK: I think our biggest 

9 challenge is taking the concepts that we're 

10 talking about today and developing practical, 

11 feasible recommendations that can actually be 

12 implemented. 

13 So the more concrete and the more 

14 specific that we can be in terms of 

15 recommendation, the more likely they are to be 

16 implemented. 

17 Briefly, both to the professors in the 

18 middle I would ask, you both talk a little bit 

19 about risk mitigation, and assuming that there 

20 are going to be harms, how do you mitigate those 

21 harms past the collection stage? 

22 What have you found to be the most 
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1 effective mechanisms for mitigating risk? Is it 

2 retention periods? Is it access controls? Is it 

3 audit trails? So what can the government do 

4 concretely to start mitigating risks? 

5 MR. SOLOVE: Well, I think it's not 

6 really just one thing that I can sort of point to 

7 like, that is it. It's all of those things are 

8 very valuable to do, everything from mechanisms 

9 to ensure that this information is accurate when 

10 information is grabbed from one context to 

11 another.

12 You know, what's accurate enough for the 

13 purposes of Amazon.com to recommend books for you 

14 is not the same level of accuracy we might want 

15 from the government. So if Amazon makes a 

16 mistake and recommends the wrong book to you, big 

17 deal. It doesn't need a hundred percent accuracy 

18 for that. 

19 But the level of accuracy differs as we 

20 differ in context. So we need to have mechanisms 

21 to make sure that when information might be taken 

22 from one context and put into the other that it's 
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1 appropriately accurate for that particular 

2 context. 

3 We need an analysis of how long we keep 

4 the data, audit trails to make sure that it's not 

5 being improperly accessed, appropriate 

6 accountability to make sure it's being kept 

7 adequately secure, and also how it's being used, 

8 controls on its use so it can't be used for any 

9 purpose ten years from now. 

10 So we need all these different things, 

11 and oversight from a lot of different bodies, I 

12 think. So it's actually a complex thing with 

13 many, many parts.

14 MR. ROSENZWEIG: There are certainly 

15 many moving parts, but from my perspective both 

16 from outside and when I was inside, since the 

17 threat that we're talking about is governmental 

18 abuse or misuse is the primary one, the principal 

19 factors that I would focus on that seem to have 

20 been effective were ones that focus on the 

21 individual government actors. 

22 Training in the first instance so that 
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1 they know the rules, inculcating a culture of 

2 compliance that is pre-error mechanisms, then 

3 obviously a lot of audit compliance work from 

4 outside inspectors general and/or Congress. 

5 And then finally, and this is perhaps 

6 where we fall down the most, the willingness to 

7 impose at least administrative sanctions on 

8 people who vary from the accepted rules, at least 

9 in a willful context, and even perhaps in a 

10 negligent context. 

11 You know, nothing attracts the attention 

12 of a government employee so much as the prospect 

13 of losing his job or being suspended for a term 

14 of months. So that would be where I would focus.

15 MR. FELTEN: If we look at the failures 

16 of compliance that have been acknowledged, we see 

17 some of them that are individual employees doing 

18 things they shouldn't, but we've also seen some 

19 that are failures of the technical systems to 

20 behave consistently with the internal policies.

21 And this is a case where oversight can 

22 operate without needing to get deeply into the 
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1 nuts and bolts of the technology, just the 

2 question of what processes are in place to make 

3 sure that your technology does what your general 

4 counsel says it should do. And I think there's 

5 an opportunity to push on oversight in that area.

6 MS. WALD: I think we'll move to Jim 

7 Dempsey now.

8 MR. DEMPSEY: Thank you, and thank you 

9 to all the witnesses. I think it's very 

10 important as we wrap up this panel to highlight 

11 what I at least heard is an awful lot of 

12 commonality. 

13 Because I think that it's important to 

14 the Board and important for the public debate 

15 moving forward not to end up with the proposition 

16 that this is all so confusing, or this is all 

17 disparate, there are so many different views.

18 I heard actually a lot of commonality 

19 among the witnesses, starting with the point that 

20 I think you all agree, whether you start from the 

21 premise that privacy is a human right or whether 

22 you start from the premise that it's an 
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1 instrumental right, I think all of you agree that 

2 it's an umbrella term which covers many different 

3 values, many different interests.

4 And I also heard agreement that the 

5 mosaic theory, even if it hasn't been accepted by 

6 courts, is real. It's real, both from a privacy 

7 perspective and it's real from the governmental 

8 perspective. 

9 MS. GOITEIN: Let the record reflect 

10 nodding.

11 MR. DEMPSEY: And thirdly, I think I 

12 heard unanimity that what the law refers to at 

13 least as the third-party doctrine, the doctrine 

14 that by giving information to one person you lose 

15 all interest, all privacy interest in that 

16 information, that disclosure to one surrenders 

17 your right with respect to disclosure for any 

18 other purpose, again, am I right there was 

19 agreement that that concept of disclosure to one 

20 is disclosure to all is not valid, 

21 constitutionality aside, for modern day reality, 

22 that doctrine just doesn't fit with the way we 
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1 view information and the way we view privacy? 

2 And Dan is nodding. Paul, would you 

3 agree that disclosure to one is not a surrender 

4 of all interest in the information? 

5 MR. ROSENZWEIG: I would say that the 

6 way that people interact today it would be 

7 inappropriate to imply consent to universal 

8 disclosure from explicit consent to disclosure to 

9 a single person, yes. 

10 I'm not sure that I would agree with 

11 what's implicit in your question, which is that 

12 it necessarily follows that that is a matter of 

13 either constitutional significance or one of 

14 legal cognizable significance that should animate 

15 this Board. I want to think about that. 

16 But I would certainly accept the premise 

17 that human experience is that if I tell Dan a 

18 secret, I'm not expecting him to tell everybody.

19 MR. DEMPSEY: In fact, there's an 

20 instrumental approach, there's an instrumental 

21 value that the disclosure of your medical records 

22 to the doctor is specifically premised on the 
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1 notion that they are, thereby you have not 

2 surrendered your privacy rights. And in fact, we 

3 want people to accurately disclose information to 

4 their doctors, therefore we promise them that 

5 their medical records, disclosure to the doctor 

6 is not disclosure to all.

7 MR. ROSENZWEIG: That's true, though of 

8 course that's a wonderful example because we 

9 accept statements made to a doctor as an 

10 exception from the hearsay rule precisely because 

11 we think that when you talk to a doctor in an 

12 emergency situation you're motivated to actually 

13 be telling him the truth. I was shot, doc. And 

14 so the doctor can in some circumstances be 

15 compelled to. So those realities work both ways.

16 MR. DEMPSEY: Can be compelled but not 

17 obviously --

18 MR. ROSENZWEIG: Not obviously 

19 collected. Not collected under, yes, HIPAA. 

20 MR. DEMPSEY: Right, yes. 

21 Also there were several witnesses 

22 mentioned the FIPPs. And I think it's, first of 
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1 all, important to say we're talking about the 

2 Fair Information Practice Principles, which 

3 actually there's no definitive version of them. 

4 But there is a version that was adopted 

5 by the Department of Homeland Security in 2008, 

6 which is as good as any, I think. 

7 And it seemed to me also that there was 

8 agreement that they are, the FIPPs framework 

9 provides the framework, the questions. 

10 They're nowhere perfectly implemented, 

11 they're nowhere fully implemented, but they are 

12 relevant as a framework for asking about how you 

13 deal with information. 

14 And then you decide, do you adjust it, 

15 does it work? If it doesn't work, do you 

16 compensate for it with more emphasis on other 

17 issues? Is that again a fair -- 

18 Paul, you're making a somewhat skeptical 

19 face, but you at least can say that it is a 

20 framework for asking the questions.

21 MR. ROSENZWEIG: It's a framework for a 

22 starting point for asking the questions, but I 
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1 think that many of those questions don't 

2 withstand the technological transitions we're 

3 going through. 

4 And so I accept it as a leaping off 

5 point, but I think I'm probably more willing than 

6 some of the other members of the board to discard 

7 some of them as inoperable under current 

8 circumstances.

9 MR. DEMPSEY: And what would you replace 

10 them with? 

11 MR. ROSENZWEIG: Well, as Ed said, 

12 emphasis on the remaining aspects and then, to my 

13 mind, I think kind of a more granular analysis of 

14 the underlying interests at stake and thinking 

15 about what the mechanisms are, the privacy 

16 interests that we're talking about is that we 

17 have to protect. 

18 Because, you know, FIPPs is kind of one 

19 size fits all, and I just don't think it kind of 

20 covers the range of the privacy interests that 

21 the chairman outlined so ably, so ably, earlier 

22 in the day.
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1 MR. DEMPSEY: Okay, thank you.

2 MS. WALD: Okay. We have a couple of 

3 questions from the audience. I'm not sure we're 

4 going to get to all of them, so what I'm going to 

5 do is direct them. I'll just be arbitrary and 

6 direct them to a particular panel member, and 

7 then if you can keep it as brief as you possibly 

8 can.

9 The first one, actually the writer 

10 wanted it directed toward you, Ms. Goitein. When 

11 a government draws data from private databases, 

12 such as telephone metadata, at which point of 

13 collection is more regulation required, the 

14 private entity's collection or the government 

15 collection from the private entity? 

16 That's a yes or no. 

17 MS. GOITEIN: I was going to say, I 

18 don't think I can answer that question. It just 

19 depends what you mean by more regulation.

20 I think obviously when you disclose 

21 certain information to your telephone company, 

22 you are in a contract with that company and that 
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1 contract regulates your dealings with the 

2 company.

3 I think one of the problems with the 

4 metadata program is that there was no reading of 

5 either the contract or Section 215 of the PATRIOT 

6 Act that would have enabled any person to know 

7 what they were consenting to and to know that 

8 their information would then go to the NSA.

9 MS. WALD: Your answer is both? 

10 MS. GOITEIN: It's both. There's just 

11 different types of regulation. There's the 

12 contractual regulation. 

13 There is some degree, I mean the Stored 

14 Communications Act is government regulation, when 

15 you get certain kinds of information from the 

16 telephone company. 

17 And then for the government there's the 

18 Fourth Amendment. And there's all manner of 

19 laws, so lots of regulation everywhere. I know 

20 that's --

21 MS. WALD: Okay, for you, Dan. I think 

22 it was Liza Goitein that said that private 
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1 companies have no incentive to coerce or imprison 

2 people, that's why perhaps the risks of injury 

3 might be greater from the government than from 

4 private companies.

5 But the writer asks, does that take into 

6 account the homeland security and prison 

7 industries? NSA couldn't do what it does without 

8 484 contractors providing IT technical support. 

9 Are there risks inherent in the increasing 

10 commercialization of national security? 

11 MR. SOLOVE: Well, yes, I definitely 

12 think problems can come from anywhere, and I 

13 don't think there's sort of inherent things that 

14 can be said about, you know, various things about 

15 where problems could be caused. 

16 I think we want to look at, you know, 

17 when does the collection and the amassing of data 

18 by the private sector cause problems? When does 

19 that access by the government create problems? 

20 And increasingly we see a cooperation or 

21 an industry in the private sector that has grown 

22 up to basically perform government functions and 
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1 help gather data, help analyze data and then 

2 share data with the government. 

3 I think all these things create various 

4 problems that we need to address. And so I think 

5 if we both keep our eye on the problems and stop 

6 looking elsewhere and just look at the problems, 

7 and we address those problems wherever they may 

8 happen, I think that's the best approach.

9 MS. WALD: Okay. Here are two, I think 

10 this must go to you, Ed Felten, could the 

11 panelists discuss what they think their Tesla, I 

12 had to ask what that was, of today should 

13 provide, what technologies of data flow analysis 

14 could or should be built in?

15 I know you've covered a great deal of 

16 this before, so if you could just give us a one 

17 or two sentence summary on it, that would be 

18 fine.

19 MR. FELTEN: In a sense the question is 

20 asking me to sum up sort of a whole area of 

21 knowledge in a few seconds, which I won't try to 

22 do.
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1 I'd simply say that as with cars, as 

2 with the Tesla, you know, some sort of high end 

3 car, you should think in terms of which 

4 technologies are available and reasonably 

5 practical to use to minimize, or control, or 

6 limit the risk of certain information practice, 

7 and then ask that those be there. 

8 You should ask that an entity that wants 

9 to collect and use the information be willing to 

10 justify the choices they've made and be willing 

11 to justify why they did not use some accepted 

12 technical, privacy-preserving technical method if 

13 it seems to be available.

14 MS. WALD: Okay. The last one is, Paul, 

15 I don't think this is in your natural bailiwick, 

16 but I'll pick you anyway.

17 MR. ROSENZWEIG: Okay.

18 MS. WALD: What about the application of 

19 privacy in quasi-federal organizations like the 

20 Postal Service or the PBGC? 

21 If I can remember back to my old 

22 judicial background, that's something benefits 
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1 guarantee corporation.

2 MR. MEDINE: Pension benefits.

3 MS. WALD: Pension Benefits Guarantee 

4 Corporation. How are they impacted by the Fourth 

5 Amendment? Are there issues and concerns for 

6 privacy in those organizations? 

7 MR. ROSENZWEIG: I suppose the honest 

8 answer would be I'm not sure. But my 

9 understanding is that the Fourth Amendment 

10 applies to those institutions insofar as they are 

11 exercising governmental authority and acting as 

12 agents for the government.

13 So I assume that Postal Service 

14 employees can't open your mail willy-nilly just 

15 because they're pseudo-private actors. I may be 

16 wrong about that, but since they don't open my 

17 mail.

18 Jim's nodding, no, I'm right. So 

19 thanks, that's good. 

20 I think that the implication of the 

21 question, which is really the most interesting 

22 part of it, so I'll transition into something I 
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1 do want to talk about, is that it emphasizes the 

2 point that Liza made, with which I do agree, 

3 which is that the line between commercial 

4 collection and government collection is 

5 increasingly blurring some, you know, and the 

6 idea that regulation of the government but no 

7 regulation of Google's collection kind of sits in 

8 dissonance. And there are these places that are 

9 halfway between.

10 For me, you know, that suggests one set 

11 of answers, because I'm unwilling to think about 

12 wholesale government regulation at an extreme 

13 level of corporate business practices. I think 

14 there's some there, but it certainly emphasizes 

15 the confluence between them.

16 MS. WALD: Okay. Well, that ends my 

17 part of the panel, unless the Chair has some 

18 parting words. 

19 MR. MEDINE: Thank you very much. 

20 MS. WALD: You've been extremely 

21 forthcoming.

22 MR. MEDINE: Thanks to the panel and for 
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1 the audience questions. 

2 We'll take a 10 or 15 minute break and 

3 we'll resume at 10:30 with the technology panel. 

4 (Off the record.)

5 MR. MEDINE: Thank you very much. We 

6 will resume and Jim Dempsey will be moderating 

7 this panel.

8 MR. DEMPSEY: Thank you, Mr. Chairman. 

9 Good morning again to members of the audience, 

10 particularly good morning to our second panel.

11 The title of our panel is Privacy 

12 Interests in the Counterterrorism Context and the 

13 Impact of Technology. 

14 I have no statement of my own, so we can 

15 go straight to the opening statements by the 

16 witnesses. I'll introduce each of them in turn. 

17 We can go down the row, which happens also to be 

18 alphabetical order. 

19 I remind the witnesses that we would ask 

20 them to keep their opening remarks to seven 

21 minutes. There is a timekeeper, which you might 

22 not have seen, but in the front row here, Renee, 
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1 who will be holding up a yellow card for your two 

2 minute warning and then a red card for time's up. 

3 Thereafter a round of questioning by the 

4 Board members, and again the possibility of 

5 questions submitted by members of the audience. 

6 PCLOB staff members throughout the 

7 audience have little index cards, and so if 

8 during the course of the panel a question occurs 

9 to you, raise your hand and someone will bring 

10 you over a little 3 by 5. 

11 Our first speaker or member of this 

12 panel is Annie Anton. She is a professor in and 

13 Chair of the School of Interactive Computing at 

14 Georgia Tech University. She has a Ph.D. in 

15 computer science, and is one of the country's 

16 leading experts on issues at the intersection of 

17 technology and policy.

18 So, Annie, please.

19 MS. ANTON: Good morning and thanks for 

20 the opportunity to testify. 

21 We're in an ever changing world where 

22 terrorists and criminals are getting smarter and 
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1 more sophisticated. Their offensive techniques 

2 are surpassing our ability to protect our nation. 

3 Providing strong technical protections for 

4 privacy and civil liberties is a counterterrorism 

5 weapon.

6 Today I focus primarily on three 

7 technology considerations. First, strong 

8 encryption is an essential technology for 

9 fighting terrorism.

10 Second, de-identification, while not 

11 perfect, may be a reasonable approach given a 

12 thorough risk analysis. 

13 And third, improved privacy threat 

14 modeling is critical for counterterrorism. 

15 Our national cyber infrastructure must 

16 be resilient to attacks from foreign powers, 

17 terrorists and criminals. 

18 Requiring government backdoors in 

19 commercial products, stockpiling zero-day 

20 exploits and weakening software security 

21 standards are all practices that weaken our 

22 nation's cyber security posture and make it 
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1 easier for attackers to infiltrate these systems 

2 for nefarious purposes.

3 The latest Apple and Google phones build 

4 in encryption by default. Both companies are 

5 configuring this encryption such that they cannot 

6 decrypt the information for anyone, including law 

7 enforcement. 

8 These measures have been sharply 

9 criticized by the Director of the FBI and the 

10 Attorney General. 

11 As a technologist, I can assert that 

12 applying security best practices such as 

13 encryption by default will yield a system that 

14 can better withstand intrusions and denial of 

15 service attacks, as well as limit access to 

16 authenticated and authorized users.

17 Requiring companies provide backdoors 

18 for law enforcement or national security hurts 

19 both individual privacy and our nation's overall 

20 security. 

21 Moreover, the security benefits are 

22 questionable at best because sophisticated 
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1 terrorists and criminals will simply use 

2 international products or more secure, less 

3 convenient alternatives. 

4 Technology and policy scholars are 

5 actively debating the merits of de-identification 

6 and anonymization techniques. The issue is 

7 critical because privacy rules only apply to 

8 identifiable data. Technology scholars emphasize 

9 that there is no way to mathematically prove an 

10 anonymized data set, that it cannot be 

11 re-identified. 

12 In contrast, policy scholars believe 

13 that anonymization provides real practical 

14 protection to most of the people most of the 

15 time. 

16 Consider that the locks on your door at 

17 home are pretty good, but not good enough to keep 

18 a determined intruder at bay. That's the idea 

19 behind practical anonymization. 

20 There are some cases where it is 

21 critical to protect a person's identity. For 

22 example, for victims of domestic abuse we need to 
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1 ensure that their location is protected and 

2 cannot be re-identified by their abuser. 

3 However, in many settings, if we apply 

4 effective but not perfect de-identification 

5 procedures, overall privacy protection may be 

6 increased and data may be more useful. In such 

7 cases the perfect should not be the enemy of the 

8 good.

9 The PCLOB might consider how to 

10 determine in practice when agencies should insist 

11 on technically strict de-identification versus 

12 when effective, but not perfect, 

13 de-identification may address the bulk of the 

14 risk. 

15 Finally, threat modeling is critical for 

16 counterterrorism, and we must improve it to 

17 achieve two goals. 

18 First, we must develop privacy oriented 

19 models. Most threat modeling techniques have 

20 been developed entirely in a security context 

21 with little privacy consideration. The latter is 

22 crucial given the rise the big data analytics and 
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1 the Internet of things. 

2 Second, as a nation we do not want 

3 insiders leaking state secrets to foreign 

4 journalists to become a common way to influence 

5 public policy decisions and debates.

6 Insiders with access to sensitive 

7 information must be considered as potential 

8 threats simply because of the extreme damage that 

9 a leak could do, either in direct cost by 

10 providing useful information to enemies, or 

11 indirect cost with respect to public relations or 

12 erosion of trust. A good threat model makes risk 

13 analysis feasible for any organization. 

14 In closing, as a technologist and 

15 privacy scholar I believe we should encourage 

16 strong encryption by default, use practical 

17 de-identification technologies now rather than 

18 wait for theoretically perfect solutions, and 

19 expand threat modeling to include privacy and 

20 security as well.

21 In addition, Ed Felten mentioned the 

22 importance of having technologists in the room. 
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1 I can't help but note that the review group did 

2 not have a technologist that the PCLOB, which I 

3 really appreciate all that you are doing, but 

4 again, there isn't a technologist in the room. 

5 And having technologists on panels is 

6 helpful, but really I would like to see us move 

7 forward to having more technologists actually 

8 involved in the decision-making. 

9 And so I'd like to thank the Civil 

10 Liberties and Privacy Board for its commitment to 

11 finding ways for the government to protect 

12 privacy, and also for meeting our critical 

13 security needs as a nation as well. Thank you.

14 MR. MEDINE: Let me just thank you for 

15 your testimony, but actually we have a 

16 technologist in the second row. 

17 MS. ANTON: Great.

18 MR. MEDINE: And we have a technologist 

19 outside as well. And so we actually do value the 

20 role of having technologists and have two full21 time on our staff.

22 MS. ANTON: Good, and I look forward to 
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1 meeting them. Thank you.

2 MR. DEMPSEY: Thank you. Our second 

3 witness is Alvaro Bedoya. Alvaro is the 

4 Executive Director of the Center on Privacy, 

5 Technology and the Law at Georgetown University 

6 Law School and was previously chief counsel to 

7 the Senate Judiciary Subcommittee on Privacy, 

8 Technology and the Law. Alvaro.

9 MR. BEDOYA: Thank you. Good morning 

10 and thank you for the opportunity to speak with 

11 you today. 

12 We have a problem right now in privacy 

13 and it's a problem for government and industry. 

14 Government and industry have developed 

15 extraordinarily powerful data analysis tools. 

16 These tools let them analyze data sets 

17 that have previously been too large or too messy, 

18 they let them process that data faster, and they 

19 let them find latent value in data sets that have 

20 previously seemed old and worthless.

21 In short, these processes create 

22 enormous value, and that value is driving both 
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1 government and industry to collect as much 

2 information as possible and to retain it as long 

3 as possible. 

4 The problem is, is that's hitting up 

5 against long-established privacy values ingrained 

6 in the FIPPs. The FIPPs encourage limited 

7 collection, they encourage data minimization, and 

8 the destruction of data that's no longer useful 

9 for the purpose for which it was collected. 

10 And so right now both in the 

11 intelligence community and in industry there's 

12 effectively an effort to redefine privacy. 

13 Privacy used to be about collecting only 

14 what you needed to collect. Under the new model, 

15 you collect as much as information as possible 

16 and you protect privacy through after the fact 

17 post-collection use restrictions.

18 I'm here to encourage you to resist this 

19 new model. In my written testimony I argue four 

20 points. The first is that collection still 

21 matters. The collection of personal data impacts 

22 a person's core right to privacy, regardless of 
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1 what happens to that data after the fact.

2 Second, this was discussed in the first 

3 panel, but there's a misconception, I think, that 

4 the FIPPs are primarily useful for commercial 

5 privacy. 

6 In my written testimony I talk about the 

7 fact that the FIPPs remain a critical benchmark 

8 against which to measure the privacy impacts of 

9 counterterrorism policies.

10 And I'll just add given the previous the 

11 discussion, that literally since their inception 

12 in 1973, the committee that wrote the report 

13 dedicated a section, it's just two pages, talking 

14 about how of course not all of the FIPPs can 

15 apply in the intelligence context, but clearly 

16 some of them must because the risk is too high.

17 Third, in my testimony I talk about that 

18 we need to remember that privacy is not about 

19 taking but about -- pardon me -- it's about 

20 taking and not about sharing.

21 And fourth and finally, I think that 

22 Americans do expect a degree of privacy in 
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1 public. 

2 Now given my limited time here I 

3 actually want to focus my oral testimony on just 

4 that first point, collection. I think it's the 

5 most important.

6 After the Snowden disclosures on the 

7 telephone records program last summer, the IC's 

8 first line of argument was that, you know, we may 

9 collect a lot of this information but we only 

10 look at a tiny part of it. 

11 The problem is that this is not how 

12 people think about privacy. If a police officer 

13 knocked on your door and said, hey, I want you to 

14 give me a list of every person you've spoken with 

15 in the last week and then said, you know, don't 

16 worry, we're really probably never going to look 

17 at this stuff, would that reassure you? I think 

18 that most people would say no. 

19 And I think that this highlights the 

20 fact that the forcible collection of sensitive 

21 data in and of itself invades what this Board has 

22 called, "the core concept of information 
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1 privacy". And that's, "the ability of 

2 individuals to control information about 

3 themselves".

4 It's not just a concept. As you know, 

5 it implicates First Amendment and Fourth 

6 Amendment interests. I elaborate that in my 

7 written testimony. 

8 But in my mind the single biggest reason 

9 to resist the privacy model that primarily relies 

10 on post-collection use restrictions is the 

11 disparate impact that that model might have on 

12 vulnerable communities. 

13 Now again, in a use restriction model 

14 you collect everything and you protect privacy by 

15 banning harmful uses of data after it's been 

16 collected. 

17 The problem is that there's basically 

18 what I'll call a moral lag in the way we treat 

19 data. 

20 What I mean by that is that we as a 

21 society are often very slow to realize that a 

22 particular use of data is harmful, especially 
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1 when it involves data of racial and ethnic 

2 minorities, LGBT people, and others who have 

3 historically lacked political power. 

4 In fact, the two most prominent examples 

5 of this moral lag involve the Department of 

6 Defense, or formerly the Department of War. 

7 During World War II, Japanese Americans 

8 volunteered information about themselves and 

9 their families in the census. They volunteered 

10 that information under a statutory promise from 

11 the federal government that that data would 

12 remain confidential. This was a use restriction. 

13 What happened? As you know, in 1942, 

14 Congress waived the confidentiality provisions 

15 and the Department of War used detailed census 

16 data to monitor and relocate Japanese Americans 

17 to internment camps.

18 After World War II a similar story 

19 unfolded for gay and lesbian service members. 

20 They were prohibited from serving openly, and so 

21 many of them turned to military chaplains, 

22 psychologists, physicians. 
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1 Yet routinely and even after don't ask, 

2 don't tell, the military would use that 

3 confidentially collected data to out and 

4 dishonorably discharge LGBT service members.

5 Now today with the benefit of hindsight 

6 we recognize that these events are 

7 discrimination, but at the time the picture was 

8 less clear for a lot of people. 

9 And that took a long time to change. 

10 The census only acknowledged the full extent of 

11 wartime sharing of census data in 2007, and 

12 Congress only repealed the ban on openly serving 

13 gay and lesbian service members in 2011. That 

14 was three years ago. 

15 So let me be clear, my point is not to 

16 cast aspersions on the Department of Defense, 

17 rather my point is that all of us as a society 

18 are consistently slow to recognize what's a 

19 harmful use of data when it comes to vulnerable 

20 communities. It often takes us decades to figure 

21 that out. Far too often today's discrimination 

22 was yesterday's national security measure. 
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1 What this means for our data and what 

2 this means for privacy is that we cannot solely 

3 rely on use restrictions.

4 What this means is that collection 

5 matters and the that simplest and most powerful 

6 way to protect privacy is to limit data 

7 collection, particularly for the government. 

8 I urge you to continue to protect that 

9 core right. Thank you.

10 MR. DEMPSEY: Thank you very much. Our 

11 next witness is Mike Hintze, who is Chief Privacy 

12 Counsel at Microsoft, where he's been for sixteen 

13 and a half years at the epicenter of the 

14 evolution of technology and privacy.

15 MR. HINTZE: Thank you. Thank you for 

16 the opportunity to speak with you today and 

17 participate in this important discussion.

18 I come to this discussion from the 

19 perspective advising on and managing privacy and 

20 related issues in the private sector. 

21 I've done that for nearly two decades, 

22 first as an associate here in a D.C. law firm, 
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1 and as you mentioned for the last sixteen-plus 

2 years at Microsoft. 

3 At Microsoft we approach the issue of 

4 privacy from a core belief that privacy is an 

5 essential value, both to us and to our customers. 

6 We have a strong commitment to privacy because we 

7 recognize that customer trust is critical to the 

8 adoption of online and cloud services.

9 Our customers, from individual consumers 

10 to large enterprises, will not use our products 

11 and services unless they trust them, unless they 

12 trust that their private data will remain 

13 private.

14 We seek to build that trust with our 

15 customers by adhering to a robust set of policies 

16 and standards. These policies and standards 

17 guide how we do business and how we design our 

18 products and services in a way that protects 

19 customer privacy. 

20 These standards are based on the Fair 

21 Information Practices, which we agree remain 

22 relevant today, including transparency about the 
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1 data and how we use it, minimization with regard 

2 to the data collected and how long it's retained, 

3 choice about collection and use of data, strong 

4 security to ensure that the data is protected, 

5 and accountability to ensure that we are living 

6 up to our commitments.

7 These standards are not just a rule that 

8 we create it and hope that our employees follow. 

9 Instead, we built them into the processes we use 

10 to operate our business.

11 For example, they're built into the 

12 tools that are used in our software development 

13 life cycle, and there are checkpoints that 

14 prevent a product or service from shipping 

15 without a privacy sign off.

16 In sum, we've taken what's often 

17 referred to as a privacy by design approach to 

18 how we operate the company and how we develop and 

19 run our services. 

20 And this approach is supported by a 

21 mature privacy program that includes dedicated 

22 personnel with privacy expertise who sit in both 
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1 centralized roles and are embedded throughout the 

2 business. The program includes incident 

3 management, response and escalation processes.

4 Further, we've developed and deployed 

5 comprehensive role-based training for engineers, 

6 sales and marketing personnel, as well as those 

7 in HR, customer service and other roles that 

8 touch and handle personal data. And our program 

9 includes executive level accountability for 

10 privacy compliance.

11 But that investment in privacy and the 

12 trust we've worked to build is undermined if 

13 those customers believe the government can freely 

14 access that data. 

15 Concern about government access to data 

16 collected by the private sector can foster a lack 

17 of trust in those private sector services. 

18 And when those concerns are focused on 

19 the access to data by the U.S. government, that 

20 lack of trust becomes focused on U.S. companies. 

21 That's why we've been vocal for the need 

22 for surveillance reform in the United States. 
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1 There have been positive steps in this regard in 

2 the last year but there's more that needs to be 

3 done.

4 We've laid out several things the U.S. 

5 government should do to help restore the trust 

6 that's been damaged by last year's revelations.

7 First, bulk data collection programs 

8 should end. We have been clear that we have not 

9 received any bulk orders, any orders for bulk 

10 data collection, but we strongly feel that 

11 surveillance should be focused on specific 

12 targets rather than bulk collection of data 

13 related to ordinary people's activities and 

14 communications.

15 The recommendations of this Board on the 

16 Section 215 program are encouraging, as are the 

17 comments of the President, and we urge the 

18 administration to end the existing program, and 

19 we urge Congress to enact prohibitions on any 

20 such orders in the future.

21 Second, we should do more to increase 

22 transparency. Transparency is a key element to 
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1 any program for protecting privacy. It 

2 facilitates accountability and enables public 

3 debate around policies and programs.

4 Here too we've seen positive 

5 developments. In particular, the government has 

6 declassified more information about its 

7 surveillance programs and the workings of the 

8 FISA court. 

9 Additionally, we and other companies 

10 filed lawsuits last year against the U.S. 

11 government arguing that we have a legal and a 

12 constitutional right to disclose more detailed 

13 information about the demands we've received 

14 under U.S. national security laws. 

15 And earlier this year we came to an 

16 agreement with the government enabling us to 

17 publish some aggregated data about the FISA 

18 orders and the national security letters we've 

19 received. 

20 It was a good step that helped foster 

21 better understanding of the type and volume of 

22 such orders that service providers received, but 
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1 we believe there can and should be more detailed 

2 reporting permitted. 

3 Third, we support reforms of how the 

4 FISA court operates. In order to foster a 

5 greater confidence in surveillance programs and 

6 government access to data that are appropriately 

7 balanced against privacy and other individual 

8 rights, surveillance activities must be subject 

9 to judicial oversight. 

10 We need a continued increase in the 

11 transparency of the FISA court's proceedings and 

12 rulings, but effective judicial review requires a 

13 true adversarial process where more than one side 

14 is heard. We urge Congress to act on FISA 

15 reform. 

16 Fourth, government should provide 

17 assurances that it will not attempt to hack into 

18 data centers and cables. 

19 In the year since the Washington Post 

20 reported an alleged hacking by the NSA of cables 

21 running between data centers of some of our 

22 competitors, there's not yet been any public 
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1 commitment by the government that it will not 

2 seek to obtain data by hacking into Internet 

3 companies.

4 We believe the Constitution requires 

5 that the government seek information from 

6 American companies within the rule of law and 

7 through authorized government access, and we've 

8 taken steps to prevent such attempts by 

9 increasing and strengthening our use of 

10 encryption across our networks and services. 

11 Nevertheless, we and others in industry 

12 will continue to press for clear government 

13 assurances.

14 Fifth, although recent government 

15 revelations have focused mainly on the U.S. 

16 government and many of the subsequent debates 

17 have focused on the privacy rights of U.S. 

18 persons, we must recognize that this is a global 

19 issue. 

20 As we seek to sell our products and 

21 services to customers around the world, 

22 discussions that focus exclusively on the rights 
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1 of U.S. persons are not enough. Many people 

2 around the world do view privacy as a fundamental 

3 human right, and they have a very real concern 

4 about whether and how governments can access that 

5 data.

6 In that regard, we appreciate the steps 

7 that President Obama announced in January which 

8 acknowledged the need to address protections 

9 about non-U.S. citizens. 

10 Along those lines in the law enforcement 

11 context, we've challenged a federal warrant in 

12 the U.S. courts seeking customer email for 

13 content that's held in our data center in 

14 Ireland. 

15 Further, we've called for governments to 

16 come together to create a new international legal 

17 framework that allows for new streamlined 

18 processes for cross border data access that can 

19 supplement existing rules. 

20 None of this should be taken to suggest 

21 that we don't value and appreciate the absolutely 

22 critical work that our law enforcement security 
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1 agencies do every day to keep us all safe. 

2 In fact, we work closely with the U.S. 

3 and other governments to help fight cyber crime 

4 and other threats. We want to ensure that those 

5 agencies have the tools and information that they 

6 need to protect us from terrorism and other 

7 threats to our safety and security, but there 

8 needs to be a balance between safety and the 

9 personal freedoms that people around the world, 

10 especially law-abiding citizens and institutions 

11 enjoy.

12 This balance is rarely an easy one. As 

13 Chief Justice Roberts recognized recently in the 

14 case of Riley v. California, privacy comes at a 

15 cost. But the court's unanimous decision makes 

16 clear privacy is an inherent and enduring value 

17 that must be protected.

18 While there's not always a perfect 

19 analogy between protecting privacy in the private 

20 sector, law enforcement, and national security 

21 context, we also, we in the private sector 

22 regularly deal with questions of striking the 
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1 right balance between privacy and other needs.

2 In each of these contexts as technology 

3 evolves we need to continually reevaluate that 

4 balance and many of the principles that have 

5 proved useful in striking and retaining that 

6 balance, the Fair Information Principles, 

7 continue to be relevant today. 

8 MR. DEMPSEY: Mike, could you wrap up?

9 MR. HINTZE: Thank you. I'll end my 

10 comments there.

11 MR. DEMPSEY: Okay, super, thanks. 

12 We'll come back to some of those issues with the 

13 questions. 

14 Our final member of this panel is Hadi 

15 Nahari. He is Chief Security Architect at 

16 NVIDIA, a company that designs and builds high 

17 performance computer systems. Hadi is a 

18 cryptographer and computer scientist. Welcome, 

19 please proceed.

20 MR. NAHARI: Thanks for the opportunity 

21 to testify today. I appreciate it.

22 I'm here as a technologist and not as a 
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1 lawyer. In Silicon Valley we say the "I'm not a 

2 lawyer" rule applies. 

3 Our concern is about building systems 

4 that are buildable and creating rules that are 

5 enforceable, so I wish to provide some technology 

6 background to the panel and to the conversation. 

7 From our perspective security is to a 

8 system what harmony is to music. Providing 

9 security as a foundation of establishing rules of 

10 privacy is our model. 

11 We build systems that are enabled and 

12 are able to enforce rules, and that is the 

13 context of security as we see it. 

14 Security is one of the intersections 

15 between technology and civil liberty, and we deal 

16 with issues such as trust and active adversary in 

17 a system. This is how we build and design our 

18 systems.

19 Our world used to be simpler, and 

20 sometimes I provide samples of that simpler 

21 world. You all remember this as a mobile phone. 

22 This is from the time that the phones were 
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1 actually doing just that, they were a phone. 

2 And some of these devices were 

3 statements of class. You all remember this, 

4 right? This was a phone. This was a mobile 

5 phone. I worked in this company. 

6 One of my favorites in the collection, 

7 text, this used to send and receive even text 

8 messages. 

9 Oh, yeah, CLIE, this was your personal 

10 and digital assistant. 

11 I have some others. Oh, yeah, Palm, 

12 they used to be a company that existed, this was 

13 one of the darlings of the valley. 

14 So these, of course this was also a very 

15 important device that everyone carried. 

16 This is from the time that the world was 

17 very simple and we built systems that did very 

18 basic things. 

19 And it was per Thomas Friedman, and I 

20 quote here, "When I sat down to write, The World 

21 is Flat, Facebook didn't exist, Twitter was still 

22 a sound, the cloud was still in the sky, 4G was a 
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1 parking place, LinkedIn was a prison, 

2 applications were what you sent to college, and 

3 Skype was a typo."

4 So June 29th, 2007, iPhone was 

5 introduced, the world changed. The world for us 

6 technologists changed, probably for everybody 

7 else in the room, non-technologist and 

8 technologists alike also changed. And we are 

9 dealing with devices that are not as simple as 

10 what we used to carry. 

11 So that's part of the problem from my 

12 perspective. I'm interested in the ramifications 

13 of the changes in this technology as the subject 

14 that we are talking about. It's only seven and a 

15 half years. 

16 It's only seven and a half years ago. 

17 So I don't believe there's any other event in 

18 history that in this short amount of time has 

19 ravaged and gone through everything and tried to 

20 change everything, such as the foundation of our 

21 society. 

22 In the old and pre-2007 world we said 
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1 things like, you cannot enumerate all the attacks 

2 in cryptography is a known statement. And state 

3 space combinatorial explosion, meaning you cannot 

4 define a secure state of a system. It was 

5 difficult back then during these devices. It has 

6 just become worse. 

7 The guarantees, we do not know anything 

8 about our future but a couple of things I could 

9 guarantee, a couple of things I could guarantee 

10 right here is that things will only get faster. 

11 We're going to build things that are faster. 

12 They're going to become smaller, a lot smaller. 

13 They're going to become cheaper, and these 

14 devices are going to become a lot more abundant. 

15 Some of them, we no longer care about 

16 building devices that are usable for a long 

17 period of time. It's a lot more economic to 

18 build these devices that are basically throwaway 

19 devices. That's the concept that we are 

20 following.

21 And they're becoming more connected. 

22 Everything is becoming more connected. You have 
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1 heard things such as IOT, Internet of things, or 

2 as I call them, thingsternet. 

3 Everything is just becoming very 

4 talkative. All of these devices are very chatty. 

5 They talk a lot. So you guys all have phones, 

6 smartphones in your pockets. From the time that 

7 I started, which was about five minutes right 

8 now, until now, each one of those devices, 

9 without you even touching them, has transmitted, 

10 sent and received, about half a meg data, without 

11 you even touching them. 

12 This abundance of information that is 

13 happening that is, without you interacting, is 

14 having a lot of ramifications on what we are 

15 doing. 

16 We heard a lot of things about data is 

17 only, you know, accumulating. It's not going 

18 away. We are generating more data than we can 

19 manage or fathom. 

20 A hundred hours of video, a hundred 

21 hours of video is uploaded on YouTube, and 

22 YouTube is not the only recipient of the service, 
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1 other companies also have these services, a 

2 hundred hours of video are uploaded to YouTube 

3 every single minute. Every single minute.

4 So we are building systems to manage, 

5 and compartmentalize, and define, and create and 

6 work with these data. And this data, as we have 

7 heard in the two panels, are not going away. 

8 They are not disappearing. 

9 In the new world, maintaining security 

10 is even harder. So as a citizen, I'm very 

11 carefully following what is happening by this 

12 esteemed Board as to what is the ramification of 

13 the decisions that we are making and whether 

14 that's enforceable, whether we can build systems 

15 that are enforcing these rules. 

16 Because right now being a security 

17 professional and creating doable and enforceable 

18 security is as unpopular as being an atheist in 

19 Jerusalem. No one likes you. So I'm hoping that 

20 we can come up with a system that is also 

21 buildable. 

22 And lastly, I close my remarks and I'm 
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1 looking forward to the questions. 

2 One more thing that I could guarantee is 

3 the attacks are going to increase only, and 

4 they're going to become simpler and easier to 

5 mount. 

6 By one measure the number of attacks in 

7 2013 were three trillion, only affecting private 

8 information, on average 27.3 dollars per attack, 

9 about a hundred billion dollars, the cost of 

10 these attacks. This data is 2013. None of the 

11 Target, Home Depot, LinkedIn, none of that 

12 information, none of those attacks are included 

13 here.

14 So with that, I close my remarks and I 

15 look forward to answering questions. Thank you.

16 MR. DEMPSEY: Thank you. We'll now go 

17 through a round of questioning, and Board members 

18 as well will be subject to the time limits here. 

19 I think I have 20 minutes and then each Board 

20 member will have five minutes, and then still the 

21 possibility of questions from members of the 

22 audience.
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1 I wanted to build my first question off 

2 of the point that I think Hadi was making at the 

3 end, which is that there seems to be this 

4 inexorable trend towards more sophisticated 

5 devices collecting, generating, sharing, emitting 

6 autonomously, automatically disclosing more and 

7 more information. 

8 And I think I'll go to Professor Anton 

9 first and then maybe come back to Hadi with this, 

10 but looking at that phenomenon and the seeming 

11 inexorability of it, the seeming inevitability of 

12 it, first on the technology design side and then 

13 on the policy side, on the technology design side 

14 what do you see as any potential at all for 

15 limiting that growth, controlling the flow of 

16 that information? 

17 You talked to some extent about the 

18 possibility of technology protecting privacy. 

19 How does that square with this tremendous ongoing 

20 growth of information?

21 MS. ANTON: Thank you. So you know, as 

22 was mentioned in the earlier panel, systems are 
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1 getting more and more complex, which makes 

2 compliance more and more difficult as well. 

3 I really hope that we don't limit growth 

4 and limit the ingenuity of new technologies that 

5 might have really great applications in the 

6 future and solve wonderful, really important 

7 problems. 

8 By the same token, there is a lot of 

9 work that's been done, especially with work 

10 that's being done at Georgia Tech, in fact, on 

11 how do we design the Internet of things or the 

12 Internet of devices, such that we are taking 

13 privacy and security into consideration, give all 

14 of the outputs, all of the possible inputs. 

15 And engineers just simply need better 

16 tools and heuristics for how to do that. And, 

17 you know, it's privacy by design, it's thinking 

18 about these things early on and not thinking 

19 about it after the fact. 

20 And in terms of controlling information, 

21 I think what we want is to secure the flow of 

22 information, but not limit the flow of 
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1 information. 

2 And these are all things that 

3 researchers are actively working on in 

4 universities and at research labs in industry as 

5 well.

6 MR. DEMPSEY: You know, I've written 

7 myself about the potential for privacy enhancing 

8 technology, value of privacy by design. But at 

9 the same time, I mean at some level I just don't 

10 see it happening.

11 MS. ANTON: So -- 

12 MR. DEMPSEY: Or let me put it this way, 

13 while I see it happening, and I think Mike 

14 Hintze's point that Microsoft has incorporated 

15 privacy by design as a corporate concept, but 

16 there are these other hugely dominant trends that 

17 almost seem to be overwhelming.

18 MS. ANTON: So within the context of 

19 counterterrorism I think that there's a lot of 

20 policies and a lot of laws that are in place. 

21 When I mentioned earlier that I'd like 

22 to see more technologists in the room, it's not 
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1 just to kind of study it after the fact, but 

2 actually to be involved in forming the policy. 

3 Because a lot of times the policy and the law are 

4 written in such a way that we can't implement it. 

5 And so what I'd like to see is more 

6 technologists involved in the discussion up front 

7 really informing the decisions about laws that 

8 are going to be passed, about the policies that 

9 we're going to adopt, because we could write them 

10 in a way that makes it a lot easier to comply 

11 with the law.

12 MR. DEMPSEY: Do you have an example in 

13 mind? 

14 MS. ANTON: Excuse me? 

15 MR. DEMPSEY: Do you have an example in 

16 mind?

17 MS. ANTON: So I work a lot in HIPAA, 

18 for instance. We have the new change with 

19 meaningful use. I had one Ph.D. student who was 

20 really working actively on how do we predict what 

21 the change is actually going to be? Because when 

22 they finally make that decision we're going to 
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1 have very little time to implement that change in 

2 systems to be able to be able to make sure that 

3 we're compliant with it. 

4 And had we had more technologists 

5 involved in that process, we'd be able to more 

6 quickly adapt our systems and we'd have a better 

7 community of practice, if you will, about how to 

8 establish those laws and how to then instrument 

9 systems to make sure that only the right people 

10 are having access to the right information at the 

11 right time and in compliance with law.

12 MR. DEMPSEY: Just to round that out, 

13 certainly you would agree that we need both 

14 better, clearer laws, as well as more mindful 

15 technology design?

16 MS. ANTON: Absolutely.

17 MR. DEMPSEY: That it's not that one or 

18 the other will solve this problem.

19 MS. ANTON: Absolutely, we need both, 

20 right. 

21 MR. DEMPSEY: I want to go to Alvaro 

22 Bedoya. There was one point in your written 
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1 testimony that you didn't mention and I want you 

2 to talk about it now. I think it's very 

3 important. 

4 A lot of our constitutional law of 

5 privacy is based upon the concept of reasonable 

6 expectation of privacy. And there's a lot of 

7 worry and a lot of, I think, legitimate concern 

8 that with these changes in technology that our 

9 expectations of privacy diminish. 

10 You talked about the fact that, in fact, 

11 with changes in technology our expectations of 

12 privacy may actually be growing. Could you 

13 explain that?

14 MR. BEDOYA: Yeah, that's exactly right. 

15 And the point here is that the Katz test cuts 

16 both ways. You know, usually when the court 

17 talks about Katz in society, they say, well, 

18 everyone's becoming inured to this idea. They're 

19 surrendering to the ubiquitous collection of 

20 their data. 

21 But I actually think people are, 

22 technology is helping people learn about what 
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1 they think privacy is. 

2 And the best example of this I think is 

3 location technology and facial recognition 

4 technology. 

5 Previously people had no occasion to 

6 develop an opinion on whether or not they 

7 expected, you know, the sum total of their 

8 movements to be developed, to be compiled in a 

9 profile, but suddenly it's becoming radically 

10 cheaper to conduct that surveillance. 

11 And so I think that in the same ways 

12 that you only realize what you had when you start 

13 losing it, for the first time a reasonable 

14 expectation of privacy in public is crystallizing 

15 in people's minds. 

16 And so I would say that ubiquitous 

17 surveillance is making people say, hey, you know 

18 what, maybe when I go to the grocery store, or I 

19 drive down the street, or I go to work I expect 

20 my colleagues at work to see me, you know, the 

21 people I know at the store to see me, my 

22 neighbors to see me, but I really don't expect 
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1 anyone to know that I'm at all those places at 

2 all times no matter where I go. 

3 And so I do think that technology can 

4 expand our expectation of privacy.

5 MR. DEMPSEY: And Mike Hintze, certainly 

6 over the past fifteen or sixteen years that 

7 you've been at Microsoft, do you think it's fair 

8 to say that your customers have become less 

9 interested and less concerned about privacy or 

10 expect more of Microsoft and other companies when 

11 it comes to privacy? 

12 MR. HINTZE: I think they expect more. 

13 I think, you know, I agree that expectations of 

14 privacy in some ways have increased. They've 

15 certainly changed. 

16 As technology evolves people learn about 

17 it, they adapt. There's certainly data sharing 

18 going on that people wouldn't have contemplated 

19 or accepted a number of years ago, but that 

20 doesn't mean people don't care about privacy 

21 anymore. 

22 It's very clear to us that our customers 
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1 care about privacy now more than ever. And you 

2 see that in the amount of resources and attention 

3 and focus that we've put on privacy. 

4 It really is one of the top legal issues 

5 we're dealing with. It's one of the top customer 

6 issues we're dealing with. We hear every day 

7 from customers who have questions about how their 

8 data is being treated, how it's being protected, 

9 how it's used. People's expectations of privacy 

10 are not fading away.

11 MR. DEMPSEY: And by the way, just to 

12 put a sort of nail in the coffin here, I think 

13 the government argues, and there's obviously 

14 Supreme Court precedent to support it, that a 

15 person surrenders his privacy rights when he 

16 discloses information to a third party such as 

17 Microsoft in the course of using the Microsoft 

18 products or services.

19 But it seems to me from what you're 

20 saying that Microsoft does not believe that its 

21 customers have surrendered their privacy rights 

22 when they use the Microsoft product or service, 
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1 and thereby Microsoft has acquired information, 

2 Microsoft does not believe that that information 

3 has zero privacy interests. 

4 MR. HINTZE: Absolutely not. On the 

5 contrary. I mean to the extent that the third6 party doctrine ever made any sense, it doesn't 

7 make any sense today. 

8 I mean people increasingly are putting 

9 all of the information that they used to keep in 

10 their homes, in their file cabinets, online in 

11 cloud services. 

12 And as recent court decisions have 

13 recognized, particularly in Riley, it's even more 

14 data. There's more data in the cloud. There's 

15 more data being created that reveal the most 

16 private and intimate details of people's lives 

17 that's in cloud services in the hands of third 

18 parties, more so than was ever in people's homes. 

19 And the expectations around privacy 

20 around that data are quite profound.

21 MR. DEMPSEY: And that's true, in your 

22 view, both of content, so to speak, and 
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1 non-content, or metadata, or transactional data. 

2 There's sensitivity there in both categories. 

3 MR. HINTZE: Absolutely. You know I 

4 don't like the term metadata because it 

5 encompasses too much. I think we should talk 

6 about what we're talking about. 

7 And you know, there's a broad range of 

8 data that's collected, or even created, or 

9 inferred through the use of online service. And 

10 some of it's fairly benign. 

11 You know, we call things metadata, put 

12 the metadata label on things like the amount of 

13 storage you're using in your online storage thing 

14 or the average file size, but even that has 

15 privacy implications. And we embrace the ideas 

16 of transparency, and consent, and all of the 

17 FIPPs around that kind of data, too. 

18 But as you go up the scale with maybe 

19 content being the end as sort of the most 

20 private, the stuff that people have the highest 

21 expectation of privacy around.

22 But other things about who you're 
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1 communicating with are right up there, right up 

2 against content in terms of what that can reveal 

3 about people's relationships, associations, 

4 thoughts, beliefs, etcetera. And there's very 

5 important privacy implications around that data 

6 as well.

7 MR. DEMPSEY: You mentioned the 

8 trans-border issues and the fact that people 

9 around the world recognize privacy as an 

10 interest, and in many cases as a human right. 

11 Just where do we stand and what are you 

12 aware of, or what do you know about, is there any 

13 progress being made multilaterally, or 

14 bilaterally in terms of developing standards for 

15 trans-border surveillance and trans-border 

16 government access, anything in the works there 

17 that we should be aware of? 

18 MR. HINTZE: Not that I'm aware of 

19 specifically. You know, there's certainly more 

20 discussions happening in recent years than there 

21 has been in the past around a number of 

22 constituents and interested parties on privacy 
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1 around the globe.

2 Jim and I were recently at an 

3 international data protection conference where 

4 these issues were loudly and vigorously discussed 

5 and debated. 

6 And so that dialogue is happening, but 

7 in terms of actual progress towards making 

8 headway in terms of developing an international 

9 framework for this stuff, there's certainly a lot 

10 more work to be done.

11 MR. DEMPSEY: May I just ask you and 

12 others, as well as members of the audience, 

13 additional panelists, if and when you do become 

14 aware of things that are making progress, please 

15 let us know. Obviously we're remaining 

16 interested in that trans-border question.

17 For Hadi Nahari, you know we've talked 

18 about privacy by design. In your experience do 

19 technologists give adequate consideration to 

20 privacy as they design products? And what more 

21 could be done to encourage or promote privacy by 

22 design?
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1 MR. NAHARI: In technology we build 

2 things that are reasonably well-defined. So I 

3 recognize in the previous panel there was a 

4 discussion that you don't necessarily need to 

5 define privacy to be able to enforce it. 

6 On the technology side, if we are able 

7 to build a model that represents a need, then we 

8 are very good at building it. 

9 I think part of the reason that mapping 

10 a very human, a very societal concept such as 

11 privacy into the devices that we build, the 

12 services that we build and we use, sometimes it's 

13 simpler, sometimes it's not.

14 To answer your question, I see a great 

15 deal of attention, a great deal of interest in 

16 the notion of privacy, privacy by design, secure 

17 by design, trustworthy by design. 

18 And especially in the field that we are 

19 dealing with, our model, in security of the 

20 device when we release it and goes to the field 

21 is a mutually distrusting system. So you don't 

22 really know. 
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1 Let me take a step back. It's one thing 

2 to build a server that resides in someone's data 

3 center where you have full control over the 

4 actual device and you have to control the flow of 

5 information, the software that is there and how 

6 it's used. 

7 It's another thing to build a device and 

8 leave it in the hands of the users and guessing 

9 what they want to do. 

10 And then it's one thing to have a notion 

11 of privacy, as we do, and build a system based on 

12 that. 

13 It's another thing when you take a look 

14 at this, should I call it a generation gap as 

15 to -- there's this company called Snapchat and 

16 they had promised that whatever picture you take, 

17 it's going to disappear. 

18 Anyone who has worked in technology 

19 knows things like this are not possible, you 

20 could simply just take a picture of that device. 

21 But we call it job security. 

22 Then when they realize that this is not 
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1 really possible they announced it, and they are 

2 under the oversight of the government for about 

3 20, I think, years to make sure that they do 

4 things right. And they are paying attention. I 

5 know they are paying a lot of attention to make 

6 sure they get things rights.

7 But then you take a look at the users. 

8 I think the stat was released last week or the 

9 week before that they asked college students, 50 

10 percent, more than 50 percent of college students 

11 said, yeah, we still will use Snapchat. They are 

12 aware. They understand. 

13 I don't know how to reconcile that. 

14 There is a new generation that has, I don't know 

15 whether it's a more or less, but certainly a 

16 different expectation and definition of privacy. 

17 And there is a vagueness of what does that mean 

18 in terms of a system that could be built.

19 Once those are, you know, in a 

20 reasonable state, we are really good at building 

21 systems that satisfy those rules. 

22 Hence my opening remarks as to our model 
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1 in the industry and in technology is we 

2 understand the rules, we are very good at, you 

3 know, creating those rules and building systems, 

4 devices, services and everything that enforce 

5 those rules, but it has to be buildable and it 

6 has to be enforceable. The attention is 

7 certainly there.

8 MR. DEMPSEY: But the first premise is 

9 the rules have to be clear and if they're not 

10 clear, then you don't know what to build.

11 MR. NAHARI: Semi-clear will do. We 

12 used to live in a world before 2007 that 

13 everything had to be really, really well-defined. 

14 It no longer exists. 

15 We have a new generation of hackers that 

16 do not abide by the rules, therefore we have to 

17 create systems that are almost right. We are 

18 seeing it in the program languages, we are seeing 

19 it in the design of the system, we are seeing it 

20 in self-correcting systems. Sometimes somewhat 

21 accurate will do.

22 MR. DEMPSEY: Do you want to respond to 
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1 that?

2 MS. ANTON: Sure. So this reminds me a 

3 little bit about what I was talking about 

4 practical encryption and anonymization. And so I 

5 think there are times in certain applications 

6 where that kind of risk is fine and there are 

7 other instances where it's not fine. 

8 And then that's where guidance from 

9 PCLOB can be very helpful in terms of trying to 

10 figure out what are the risk profiles and when is 

11 it that we can have pretty good rules and when do 

12 we have to have very, very tight, accurate, 

13 hundred percent certainty kind of rules.

14 MR. DEMPSEY: Okay, thank you. At this 

15 point other members of the Board will pose some 

16 questions under the five minute rule. And we'll 

17 go in sort of reverse order down the line here 

18 starting with Rachel Brand.

19 MS. BRAND: Thank you, Jim, and thanks 

20 to all of you for being here. 

21 That's actually a really good segue 

22 because the first question I was planning to ask 
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1 was, Dr. Anton, I was interested in what you were 

2 saying about not letting the perfect be the enemy 

3 of the good in terms of de-identification. In 

4 the domestic violence context you want it to be 

5 perfect perhaps, and in other contexts good 

6 enough will do. 

7 Can you explain what you mean by that? 

8 What's an example of a de-identification method 

9 that might be good enough but perhaps not 

10 perfect?

11 I'm not a technologist, as you know, so 

12 if you can help me out, that'd be great.

13 MS. ANTON: All right. So there are 

14 certain cases of studies that have been done, for 

15 instance, when the Netflix put out their data 

16 online and then researchers went and looked at 

17 the Internet Movie Database to try to see whether 

18 they could re-identify people. They had 

19 resources, it was readily available information. 

20 In this context I don't think anyone was 

21 personally hurt by it. 

22 But there might be cases where that kind 
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1 of identification could be extremely damaging. 

2 And so the more, we talked earlier about 

3 aggregation of databases and how the ability to 

4 link different kinds of information across 

5 different kinds of databases could actually be 

6 detrimental. It can also help us find the bad 

7 guy though. And so that's the tension, right?

8 So when is it okay and when is it not 

9 okay? And are there instances, for instance, for 

10 Netflix or something that's available online 

11 that's just not, you know, where you went to 

12 school or something that's not very important. 

13 It may not be really necessary to worry about 

14 where you had dinner, for instance.

15 But in a context of a group that is 

16 actively trying to announce a terrorist attack, 

17 then that's really important.

18 MS. BRAND: So I guess that makes sense 

19 in terms of when it's important and when it's not 

20 important, but how do you do it? I mean like, 

21 for example, how do you do the perfect in the 

22 domestic violence context?
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1 MS. ANTON: I think that's very 

2 difficult. I think we have technology that's 

3 pretty good but not perfect. And so the idea is 

4 do you keep the data unencrypted and then easily 

5 accessible, because it's not very important, or 

6 do you actually encrypt it and then use 

7 reasonable, practicable anonymization on top of 

8 that? 

9 So it just depends. And I think this is 

10 one of those cases where technologists would 

11 welcome guidance in helping us to figure out what 

12 are the risk profiles, because technologists 

13 don't have access to sometimes what the risks are 

14 within a counterterrorism context.

15 MS. BRAND: For Mr. Bedoya, you said 

16 something along the lines of, in the national 

17 security context some of the FIPPs must apply 

18 even if they all can't. Can you elaborate a 

19 little bit more?

20 MR. BEDOYA: Yeah, sure. So the first 

21 is a historical point, which is that when the HEW 

22 report was issued, I was just reading, it's like 
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1 pages 74, 75, the committee actually says, okay, 

2 we just set out these standards, clearly all of 

3 them can't apply to all intelligence records, but 

4 some of them must apply because the risk is too 

5 high if we don't have some protections. 

6 So to put that more concretely, 

7 obviously the difficult ones are individual 

8 participation and transparency. And I think 

9 there are ways to address these, at least on an 

10 aggregate level that would be really powerful.

11 So, you know, I think in the 702 context 

12 the Board has -- and to take a step back, I think 

13 it is shocking that one and a half years after 

14 the Snowden disclosures the American public 

15 doesn't have even a rough sense of how many of 

16 them have had their information collected. 

17 Take the telephone records program, 

18 people think it's everyone, but then you have 

19 news reports saying actually only 30 percent of 

20 calls are actually recorded. 

21 And so in the 702 context the Board has 

22 recommended various measures to identify the 
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1 scope. In all my time in the Senate I never saw 

2 anything that would lead me to believe that it 

3 would actually be impossible for the NSA to 

4 produce an estimate based on statistical sampling 

5 of the number of U.S. persons collected in 702 

6 data.

7 In the 12333 context there's a number of 

8 things you could do to quantify scope. One of 

9 them could just be releasing the number of 

10 queries done on USP data and 12333 data. 

11 So I think there are ways to address 

12 these principles at the aggregate level, if not 

13 at the individual level.

14 MS. BRAND: Okay. Anybody else have a 

15 thought? 

16 MS. ANTON: I have a thought on that in 

17 terms of transparency. This is another way in 

18 which, for instance, FISC technologists could be 

19 helpful because when you have -- if Hadi whispers 

20 in Mike's ear, I spoke with Jim Dempsey about the 

21 panel, by the time that gets to Jim it's going to 

22 be, I spoke with Jim about wearing flannel. It's 
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1 going to be something completely different.

2 So when you get lawyers talking together 

3 from the NSA and the FISC about technology, and 

4 you don't have a technologist there to ask 

5 questions or make suggestions about, well, we 

6 could actually, have you thought about including 

7 this kind of metric, or collecting this kind of 

8 data, or instrumenting the software in certain 

9 ways, we could actually improve the ability to 

10 have more transparency and more oversight in 

11 technology with those discussions, bringing 

12 everyone in the room.

13 MS. BRAND: Thank you.

14 MR. DEMPSEY: Chairman Medine.

15 MR. MEDINE: I'm going to try to get a 

16 question in for each panelist, so I'd appreciate 

17 brief responses.

18 For Annie, you said something that 

19 surprised me a little bit, which is that 

20 encryption is good for counterterrorism. 

21 And I guess I would like to understand 

22 more. I understand having or mandating a 
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1 backdoor weakens protections, but why? It would 

2 seem as though terrorists can now hide their 

3 communications, which seems to be detrimental to 

4 counterterrorism.

5 MS. ANTON: I think it's a better world 

6 when everyone can hide their information. And so 

7 there was a case in Greece where there was a 

8 phone and someone was able to actually start, 

9 because of the backdoor and the known exploits, 

10 they were able to actually listen to the 

11 conversations, basically do a wiretap on the 

12 prime minister. That's what happens when you 

13 don't have encryption and you don't have security 

14 by default.

15 And so to think that the terrorists 

16 aren't going to do the same thing, I think is 

17 naive. 

18 MR. MEDINE: Alvaro, you talked about 

19 the expectation of privacy, and if I heard you 

20 correctly, but tell me if I'm wrong, is that 

21 you're in a sense suggesting that we talk about 

22 not what people expect their privacy to be, 
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1 because I can put up a sign saying I'm conducting 

2 video surveillance and I can destroy that, but 

3 their expectations of what privacy should be, a 

4 more normative standard.

5 MR. BEDOYA: So I'm actually not saying 

6 that. So that's a separate wonderful, powerful 

7 argument. 

8 What I'm saying is that technology is 

9 making us realize that we do expect privacy in 

10 scenarios that didn't exist ten or 15 years ago. 

11 So I think technology can expand our notion of 

12 privacy. 

13 But I also think that the Fourth 

14 Amendment doesn't just protect me and you, it 

15 protects us as a society and it sets a base for a 

16 relationship between a government and its 

17 citizens that also needs to be protected.

18 MR. MEDINE: Okay. And I guess this is 

19 for Mike, the Fourth Amendment, which is you 

20 talked about the balance between government 

21 requests and your customers' privacy. Do you 

22 think the government should have a warrant every 
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1 time it accesses your customers' records, 

2 particularly if they're American customers? 

3 MR. HINTZE: Yeah, I mean certainly in 

4 the law enforcement context we've advocated for a 

5 reform of that that would in effect require a 

6 warrant for access to any content, regardless of 

7 the age, to precise location information, other 

8 sensitive data. 

9 You know, I'm not sure we would go so 

10 far as to say that a warrant is required in every 

11 single case for every single data type, but we 

12 certainly need to update the rules so that there 

13 is appropriate judicial review of surveillance 

14 programs and specific requests that we get for 

15 data.

16 MR. MEDINE: So in terms of the third17 party doctrine, would you then essentially not 

18 have it be an absolute exception to the Fourth 

19 Amendment, but essentially where would you go 

20 with it to provide some protection, but not 

21 necessarily a full warrant protection?

22 MR. HINTZE: Yeah, I mean the laws that 
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1 we deal with in the law enforcement context 

2 provide a sliding scale, in effect. 

3 I mean 2703(d) orders provide some 

4 reasonable oversight and protection, something 

5 below warrant and probable cause, and we've taken 

6 the position that that's appropriate for some 

7 types of subscriber data, etcetera.

8 MR. MEDINE: Thanks. And, Hadi, you 

9 talked about, and I want to put this in the 

10 context of how much information should be 

11 collected, and you talked about enforceable rules 

12 for collection, but you also said that collection 

13 is going to be faster, cheaper, and we're going 

14 to be all more connected, and that attacks will 

15 increase, and that even compliance with rules may 

16 be more difficult. 

17 Professor Felten talked about potential 

18 abuse of information and also the increased 

19 possibilities of breach. 

20 How would you strike the balance between 

21 collection rules and essentially use rules? 

22 MR. NAHARI: That's a very difficult 
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1 question, a very difficult one. I don't know if 

2 in the technology side of the house, I don't know 

3 if we really know where the balance is. 

4 We take a look at the attacks, we take a 

5 look at the system, we take a look at the 

6 capabilities, we take a look at the mere fact 

7 that all of these attacks, all of these exploits 

8 are becoming so advanced that I used -- to give 

9 you one concrete example, I used to need to be 

10 physically around your things that you touched to 

11 be able to lift your fingerprint and then have 

12 access to your phone and then use that 

13 fingerprint to mount an attack and use your 

14 biometry. 

15 With the resolution of the cameras that 

16 we have these days, sometimes with a very high 

17 resolution camera, I just need to have your 

18 picture that was taken somewhere in China to be 

19 able to zoom and zoom and zoom and then lift your 

20 fingerprint and mount an attack.

21 Now, how do you reflect things like this 

22 as to should we build systems that whenever 
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1 there's a fingerprint, it smudges it and we don't 

2 expose it? There are things like this that I 

3 encompass all of those use cases as it should be 

4 buildable. 

5 But what I'm trying to get across is 

6 coming up with the rules that define those 

7 capabilities or things that should be and 

8 shouldn't be done is a very complex problem.

9 MR. MEDINE: Thank you.

10 MS. COLLINS COOK: So thank you guys for 

11 another excellent panel. 

12 My first question, and this goes back to 

13 what I had said on the previous panel, which is I 

14 view our job to be translating these ideas, these 

15 concepts, these concerns into practical 

16 recommendations. 

17 So starting with you, Mr. Hintze, what 

18 have you found effective as a privacy officer to 

19 ensure your very large workforce, your 

20 complicated workforce dealing with emerging 

21 issues takes privacy seriously, your rules are 

22 enforced, and that from beginning to end privacy 
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1 is a part of your culture? 

2 Because we have a new NSA privacy 

3 officer, so this is free advice to the new 

4 privacy officer over at NSA. 

5 MR. HINTZE: Well, thank you. You know, 

6 as I alluded to in my opening remarks, you know, 

7 one, there's no silver bullet. You need to take 

8 a number of approaches. 

9 And we've taken a number of approaches 

10 to drive awareness and sensitivity around privacy 

11 throughout our workforce through a number of 

12 steps, some mandatory training that's required 

13 for all employees that cover a range of ethical 

14 and compliance issues, deeper role-based training 

15 that's specific to software engineers, that's 

16 specific to sales and marketing people, that's 

17 specific to different roles that people play in 

18 the company that impact customer privacy. 

19 We have, as I mentioned, not just sort 

20 of told people what the rules are and then 

21 crossed our fingers and hope they abide by them. 

22 We have put in checkpoints in the way 
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1 that we have developed our internal systems, the 

2 way you develop a software and get it out the 

3 door that has to go through certain checkpoints 

4 and reviews to ensure that privacy issues aren't 

5 missed or overlooked. 

6 So there's a number of things we've done 

7 along those lines to make sure that people are 

8 aware and have the tools available to them to do 

9 privacy right. 

10 But then there's also different checks 

11 along the way to ensure that mistakes don't get 

12 made. 

13 And nothing's perfect of course, but we 

14 try to do a multifaceted approach, or a 

15 multi-layered approach to make sure that we catch 

16 those things.

17 MS. COLLINS COOK: And so let me follow18 up on this, and it's a somewhat specific example 

19 but hypothetical. 

20 Have you found training to be more 

21 effective or effective enough in the absence of 

22 pairing with mechanisms and processes? 
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1 That was a horrible question, so I'm 

2 just going to start over again.

3 So 702, that program has certain legal 

4 requirements. In the privacy sector would you 

5 train to those legal requirements or would you 

6 also have, for example, when an analyst is 

7 sitting there attempting to target, or select, or 

8 whatever they're going to do, also have at each 

9 stage of the screen, or the process, or however 

10 they're doing it, rules reflected in the computer 

11 system that they're attempting to use?

12 MR. HINTZE: We do both. To the extent 

13 that you can use technology to enforce policy 

14 that's always super effective because you get 

15 past or you reduce the potential for human error. 

16 But that's not always possible. You 

17 can't completely prevent mistakes, oversight, or 

18 intentional bad acts. And so you need to do more 

19 than that. 

20 You have to have, you have to build the 

21 awareness so that the inadvertent stuff is 

22 reduced. You have to build in the technology 
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1 tools to try to prevent that from happening. 

2 And then you need some level of checks 

3 to make sure that everything went right. And if 

4 it's, you know, somebody who's intentionally 

5 trying to circumvent a policy for whatever 

6 reason, that there's some way to catch that 

7 before it creates a negative impact.

8 MS. COLLINS COOK: So I think I have 

9 time for one other quick question. 

10 In the Section 215 program one of the 

11 features was, in fact, that not all of the call 

12 detail record went to the government. In fact, 

13 names are not provided originally to the 

14 government, and subscriber information, simply 

15 numbers to numbers. Would that be an example of 

16 de-identification and anonymization?

17 MR. ANTON: Sure.

18 MS. COLLINS COOK: That was my only 

19 question.

20 MS. WALD: I have a couple of very sort 

21 of brief questions, which I think you can answer 

22 very quickly and that way I'll get them all in.
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1 I'll begin with Annie. You talked about 

2 how it would be good for us, and we already do 

3 have technologists on the Board. I'll ask the 

4 government when we have the government board 

5 here, too, but let me ask you based upon your 

6 knowledge here, does the government have 

7 technologists who worry at all about privacy? 

8 I know they have technologists 

9 obviously, but is this, as a result of your 

10 observations and study in the field, something 

11 that they consult with the technologists about, 

12 hey, we need this kind of information for 

13 national security, but we'd like to get it or as 

14 much as we can, what's the balance? Does any of 

15 that kind of thing go on inside the government 

16 with technologists? 

17 MS. ANTON: Right. So having worked a 

18 lot with the government I know that they consult 

19 technologists greatly with security, with 

20 privacy, with compliance issues, and how do we 

21 engineer software that takes all of that into 

22 consideration. 
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1 I think if we look at the past five 

2 years or so, or six years or so that you'll see 

3 that the NSA was really, really focused on 

4 compliance. I think the results of the reports 

5 and the oversight has shown that they've done a 

6 really good job with that. When there's been an 

7 issue, they've dealt with it. 

8 I think someone mentioned the new CPO at 

9 NSA. I think what we'll see different now is 

10 that not only is the, are we complying with law, 

11 going to be something that's factored into all of 

12 the software that's developed and all of the 

13 tools and the techniques and the procedures, but 

14 also now well, just because it complies with law 

15 should we really be doing it, and what's the 

16 extra step we're going to take to really consider 

17 privacy at the onset? 

18 MS. WALD: So you sound reasonably 

19 satisfied with the fact that they're taking it 

20 seriously and doing the best they can? 

21 MS. ANTON: I absolutely do. I wish, I 

22 actually feel very comforted by the fact that the 
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1 government has a ton of oversight and a ton of 

2 laws to comply with. 

3 I personally am much more worried about 

4 the large collection, amount of collection that's 

5 taking place in industry that people don't really 

6 understand.

7 MS. WALD: All right. So I can go on to 

8 my next.

9 Mr. Bedoya, you talked about how 

10 important it was to limit collection to what was 

11 necessary or purposeful, etcetera, but in light 

12 of so many of the experts on both panels have 

13 talked about almost like an almost inevitable 

14 momentum of collection, collection, collection, 

15 where would you look, what part of the government 

16 or where would you look for the mechanism to try 

17 and limit the collection, or get that kind of 

18 impediment or balance done?

19 MR. BEDOYA: Certainly. So I think 

20 folks have been saying that it's inevitable that 

21 industry is going to collect all this data. I 

22 don't think folks have been saying that it's 
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1 inevitable that government will collect it. 

2 And I, for one, don't actually think 

3 it's inevitable that industry will collect it. 

4 But taking that as a given, I think the 

5 question is about reconstructing the firewall 

6 between government and industry with respect to 

7 data collection. 

8 And so I'd be surprised if anyone on the 

9 panel thinks, or on the previous panels thinks 

10 that it's inevitable the government will collect 

11 all this data. 

12 One quick other point, Judge Wald, on 

13 your previous question, I should note that I 

14 believe that the congressional committees that 

15 conduct oversight on FISA and on foreign 

16 intelligence, certainly the Senate Judiciary 

17 Committee lacks a technologist, and I think 

18 that's an issue that needs to be addressed.

19 MS. WALD: I think we talked a little 

20 bit about that in our first report on FISA 

21 reform.

22 Okay, Mr. Hintze, you talked earlier, 
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1 you said one of your principles was there 

2 shouldn't be any bulk data collections. 

3 Now terminology is varied all over the 

4 place, so it would help me if I knew what you 

5 meant by bulk collection there. 

6 And let me just tell you, one gathering 

7 of public health people and they talked about the 

8 great importance of public health data, you know, 

9 especially for when epidemics come along or that 

10 sort of stuff. 

11 So wouldn't some of that come under your 

12 ban against all bulk data collection? 

13 MR. HINTZE: I was talking specifically 

14 about government surveillance programs that come 

15 to industry.

16 MS. WALD: Okay, I just wanted to 

17 clarify that because -- and what do you mean by, 

18 give us an example of what you would call bulk 

19 data. Because this has been a debate as to 

20 whether this program or that program falls under 

21 bulk data.

22 MR. HINTZE: Certainly. I had in mind 
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1 the 215 program in particular where government 

2 goes to service providers.

3 MS. WALD: Where it's not targeted? 

4 MR. HINTZE: Yes, it's not targeted, 

5 correct.

6 MS. WALD: I think that's all I have 

7 right now.

8 MR. DEMPSEY: We may be able to go back 

9 to Board members for additional questions. I 

10 would like to continue with this panel up until 

11 the top of the hour. 

12 We have one question from the audience 

13 which I will read, and we welcome others if 

14 others want to pose questions.

15 In 2005, the National Academy of 

16 Sciences studied whether pattern-based data 

17 mining can anticipate who was likely to be a 

18 future terrorist. It concluded that this wasn't 

19 feasible. 

20 And the question is, is pattern-based 

21 data mining in the terrorism context, is it 

22 feasible today and will it be feasible ten years 
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1 from now? Would anybody like to address that? 

2 Hadi?

3 MR. NAHARI: I don't know specifically 

4 about terrorism. I'm mindful of what Ed 

5 mentioned is that we have limited data. 

6 But there is a program that has been 

7 running in Los Angeles in LAPD. We may not 

8 necessarily still be able to identify specific 

9 criminals, but our predictive modeling systems 

10 have been at work. They're able to make a 

11 reasonably good prediction about where the 

12 criminal activities are more likely. 

13 It is not precisely the question that 

14 you're asking, but I can assure that it is just 

15 becoming better. I can assure that any service 

16 provider that has the amount of data that we are 

17 generating, and it's becoming more and more and 

18 more generated, is just honing and fine tuning 

19 and polishing their models. 

20 Whether it's going to be applicable to 

21 antiterrorism methods, I don't know. I think all 

22 of these models are heavily data-driven. So one 
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1 would need a lot of data. 

2 But to the point that these models, 

3 these predictive modeling are able to predict 

4 things may relate indirectly to terrorism or 

5 criminal activities, the systems are suggesting 

6 that we are going that way.

7 MR. DEMPSEY: Other thoughts on that 

8 question? 

9 There's a system in Chicago that the 

10 Chicago Police Department has deployed, which 

11 both has been touted and criticized, but it does 

12 somewhat at the neighborhood or block level 

13 predictive or predictions as to criminal activity 

14 as well as, I understand, individual level, 

15 identifying people who may be either victims of 

16 crimes or perpetrators of crimes. Again, both 

17 touted and highly criticized. 

18 Any thoughts or comments? 

19 MR. BEDOYA: One just quick one, which 

20 is the risk of creating a feedback loop. You 

21 know, if you predict that there will be crime on 

22 corner X, you watch corner X like a hawk, you see 
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1 every crime that occurs on corner X and you 

2 therefore draw an over-represented sample of 

3 crimes at corner X, reinforcing your prior 

4 conviction that you thought corner X was real 

5 dangerous. So that's the main one from my 

6 perspective.

7 MS. ANTON: So this is certainly not 

8 necessarily my area of expertise, however 

9 predictive is different from being able to 

10 reconstruct after the fact. And so can we use 

11 these things to then, when something has 

12 happened, go back and find whether we missed 

13 certain people that are still involved? Yes, I 

14 do believe that's the case. 

15 In terms of predictive, I think we have 

16 a ways to go. By the same token I get, every 

17 morning I get a crime ratings, a crime report for 

18 all the crime in my area. And I can tell you, I 

19 can predict where there's going to be, on a 

20 weekly basis, crime in my neighborhood. So, you 

21 know, we're getting there.

22 MR. DEMPSEY: Well, I mean on some level 
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1 that's just Comstat all over again, the systems 

2 that have been available to police for decades. 

3 MS. ANTON: Sure.

4 MR. DEMPSEY: One question, and I'll go 

5 down the row again, and I'll pose the question 

6 and I think we can just go down the row with 

7 additional Board members if they have, the Board 

8 members have additional questions. 

9 I had said in talking to each of the 

10 panelists that I didn't want this to be a panel 

11 about going dark and the implications of 

12 encryption, but several of you have alluded to 

13 encryption and its significance here, and I would 

14 ask any of you who would, to comment on the 

15 following, which is, there is a growing trend 

16 towards more and more devices, cheaper and 

17 cheaper wearables, and the Internet of things, 

18 and more and more data collection occurring. 

19 There's also it seems a trend towards 

20 more encryption by default, whether it's at the 

21 device level or, as Mike Hintze was referring to 

22 in terms of the encryption of data flowing 
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1 between data centers. 

2 So it seems to me like we have two 

3 things going on at once, which is not unusual. 

4 Somebody referred to the modern era, the era of 

5 the Internet of things, big data, ubiquitous data 

6 flows, as the golden age of surveillance. 

7 And it seems to me that both trends will 

8 always be there. More and more information 

9 available both to the private sector and possibly 

10 to the government, and increasing pervasiveness 

11 or at least increasing diffusion, if not 

12 comprehensive diffusion of encryption. 

13 Comments on that as a premise, first of 

14 all, the premise of my question, am I right? 

15 And then secondly, where does that leave 

16 the government, and would you agree with my 

17 assumption that there will still be huge amounts 

18 of information available, both to the private 

19 sector for its purposes, as well as to the 

20 government? 

21 I guess let's go right down the row. 

22 Professor Anton?
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1 MS. ANTON: So I believe that there will 

2 still be a lot of data that's available to 

3 government. When I say that I really support 

4 encryption by default, I also really think that 

5 our country really, we were the code hackers, and 

6 it was really critical in World War II. 

7 And I think that instead of just kind of 

8 taking the lazy approach and saying, oh, leave us 

9 a backdoor, that we should just get better at 

10 cracking the code, because they're getting 

11 smarter and we need to get smarter, too.

12 And so I leave it to the lawyers to 

13 decide what the legality of when you can actually 

14 apply that or break into a system. 

15 But being satisfied with just having a 

16 backdoor means that we're not advancing our state 

17 of the craft and our tradecraft here in this 

18 country and we're going to be left behind as a 

19 result.

20 MR. BEDOYA: I'll actually pass.

21 MR. DEMPSEY: Yeah, my thoughts on this, 

22 two trends seem to be occurring simultaneously.
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1 MR. HINTZE: Yeah, I mean we're 

2 certainly seeing an expanded use of encryption, 

3 encryption between customers and the service 

4 provider, and encryption between data centers, 

5 encryption on devices, etcetera. 

6 And that's being driven by customer 

7 demand. I mean customers are concerned about the 

8 security of their data. And they're not just 

9 concerned about the security of their data 

10 vis-a-vis hackers and bad guys, they're 

11 increasingly concerned about the security of 

12 their data vis-a-vis governments. 

13 And so to the extent that there is that 

14 concern out there that's driving customer demand 

15 for these security features and companies will 

16 continue to invest in that. 

17 Does that mean that there will be no 

18 data available? I don't think so. I mean the 

19 nature of many cloud services requires service 

20 provider access to it. 

21 You can't run an effective email system 

22 without being able to filter the content for spam 
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1 and malware. 

2 And so there will be a point in the 

3 communication chain where data is available, and 

4 that means that if it's available to a service 

5 provider, it's available to a government through 

6 lawful demands. So I guess that's it.

7 MR. DEMPSEY: Hadi, any thoughts on 

8 this, and then I'll yield.

9 MR. NAHARI: First off, I want to agree 

10 with Dr. Anton's point, we should just get 

11 better. We cannot ask industry, oh, don't 

12 encrypt, don't do anything. I would love to 

13 follow that when Chinese and Russians also follow 

14 that as well. So that's just not going to work. 

15 I'm very respectful of the problems that 

16 the law enforcement agency has with the current 

17 state of affairs. We just have to get better. 

18 And it works, at the end it's going to 

19 work better for us as a nation. So that's number 

20 one, I fully agree.

21 Some of the things, so going dark, I 

22 don't know if it's going dark. I know that we 
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1 are currently in a state that we are really able 

2 to think a certain way about the system design, 

3 about the system security, about maintaining 

4 privacy, that world has changed. 

5 The world and the industry has changed 

6 rapidly. The rest of us are catching up. So I 

7 think it pays dividend if we figure out, take 

8 some time, figure out what are the rules of this 

9 new world where we don't necessarily need to rely 

10 only on encryption. 

11 I'm a big fan of encryption. I think 

12 it's one of the tools that security professionals 

13 and everyone has, but there are others. The fact 

14 that some data is encrypted is not on its own 

15 necessarily the end of the world. 

16 I mean how many times, I know Michael 

17 mentioned that we are overusing this notion of 

18 metadata, but if you think about metadata as 

19 something about the data, it is meaningful when 

20 you see some encrypted data is being accessed a 

21 little bit more than the other. One could 

22 discern, one could learn things about it.
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1 Once we start learning how to deal with 

2 this system, then we could maintain encryption, 

3 then we could maintain stronger encryption. We 

4 could also deal with the cases where we don't 

5 have access to clear. 

6 I think our law enforcement, I think our 

7 government, I think our legal system, I think us 

8 as a society are in the process of learning how 

9 to deal with this new world were things that we 

10 knew in the past no longer apply.

11 Lastly, the new generation have figured 

12 it out. I think they're doing a lot better. 

13 They're figuring out that you cannot expect 

14 everything is going to be fully protected for 

15 you. They're figuring out ways to live in the 

16 world where they're posting a lot of things on 

17 Facebook that, I mean us probably won't do. 

18 They're trying to learn how to deal with 

19 a system that, you know, you may not have the 

20 capabilities of asserting your privacy in the way 

21 that our generation did, but still have an 

22 expectation about their rights.
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1 MR. DEMPSEY: Does a particular Board 

2 member have a question? Yes?

3 MS. BRAND: Several of you have referred 

4 to oversight in one way or another and I just 

5 want to ask a question about that. 

6 In my view, oversight is especially 

7 important in the intelligence context because of 

8 the necessary level of secrecy that attends. 

9 It's important in all areas of government, but 

10 especially here. 

11 But at the same time, when you start to 

12 layer on box checking exercises and paperwork 

13 there is a point of diminishing returns and you 

14 sort of have oversight for its own sake that 

15 doesn't actually deter misconduct or ensure 

16 compliance with the rules. 

17 Do any of you have thoughts on 

18 principles for what's effective oversight, as 

19 opposed to just another box checking exercise?

20 MR. BEDOYA: So I certainly have a few 

21 thoughts for the legislature. I think that 

22 there's been a lot of soul searching around how 
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1 the executive needs a change in practices with 

2 respect to internal oversight. But I think 

3 there's some pretty serious problems at the 

4 legislature. 

5 One of them is the technologist issue 

6 that I mentioned. Another is clearances. I can 

7 say with moderate to high confidence that most 

8 United States senators lack a staffer with TSSI 

9 clearance. I hope I'm wrong. I don't think I 

10 am. 

11 And the fact is that all of the key 

12 briefings for these senators are conducted at 

13 that level. And as a staffer, I know there's a 

14 lot of staffers in the room, you don't send your 

15 boss into a meeting about soybeans without a TSSI 

16 staffer -- sorry, no, you don't need a TSSI 

17 staffer for that, but you don't send them into a 

18 meeting on an issue that seems very easy without 

19 a staffer. And a lot of these folks are going in 

20 on staff.

21 Now thankfully folks on judiciary and 

22 intel have dedicated TSSI folks for the committee 
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1 that they can rely on, but outside of those 

2 committees you're often flying, I don't want to 

3 say flying blind, but you don't have the 

4 resources you need to actually conduct that 

5 serious oversight.

6 MR. MEDINE: I have two questions for 

7 Professor Anton on de-identification. 

8 One is you commented earlier that phone 

9 numbers without names associated with them would 

10 be de-identified information -- 

11 MS. ANTON: It's actually not 

12 de-identified, because if it's my cell phone, I 

13 stand corrected on that.

14 MR. MEDINE: Okay. Because obviously 

15 the availability of reverse directories makes 

16 that --

17 MS. ANTON: Absolutely, sorry.

18 MR. MEDINE: Then I guess you also had 

19 commented earlier that by analogy of having a 

20 lock on your door was a pretty good protection 

21 against burglars but obviously not a perfect 

22 protection.
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1 And I guess the question is, in the 

2 context of a massive database burglars may not 

3 have the incentive or wherewithal to break into 

4 everyone's home in a community, but with a 

5 massive database with a brute force attack, you 

6 might be able to get a very valuable return on 

7 it. 

8 So does that suggest that 

9 de-identification needs to be essentially 

10 stronger or may not even be sufficient? 

11 I mean as you pointed out on the Netflix 

12 example, and Professors Paul Ohm and Latanya 

13 Sweeney have written articles about the ability 

14 to de-identify, is it a useful tool in some 

15 instances but not others? 

16 And even where it's useful, does it to 

17 have to be a pretty enhanced form of 

18 de-identification? 

19 MS. ANTON: Well, I think it's better 

20 than nothing. You have to work harder at it to 

21 get access to it, right, and to really be able to 

22 understand it. But that's going to help us with 
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1 the, you know, high school kid who's just trying 

2 to tinker around, right?

3 But I think this is another example 

4 where encryption is really, really important, and 

5 very strong encryption. And so I think it's a 

6 blend of both.

7 MR. MEDINE: Thank you.

8 MS. COLLINS COOK: Just on the issue of 

9 de-identification and anonymization, I had 

10 understood it as a concept that could apply in 

11 varying degrees. So at a period of time it has 

12 been de-linked from the identifying information 

13 and now they have to go to court in order to 

14 re-associate it with the identifying information.

15 So I don't think I was asking you to say 

16 that it had been permanently de-identified or 

17 anonymized.

18 This question is for Mr. Bedoya. To the 

19 extent that we're looking at evolving standards 

20 or evolving notions of expectations of privacy, 

21 how do you quantify it? 

22 Is it because 51 percent of folks in a 
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1 Washington Post poll said I care about this but 

2 I'm still using Facebook? Do you look at 

3 conduct? Do you look at the fact that people 

4 inside the beltway really care? People in ivy 

5 leagues really care? I struggle with what is a 

6 good way to identify emerging notions of 

7 expectation of privacy.

8 MR. BEDOYA: I'm not going to pretend to 

9 know the right answer to that question. It's a 

10 really, really hard question. 

11 I certainly think that looking at 

12 conduct is extremely valuable, and there's been a 

13 lot of discussion about the third-party doctrine. 

14 And the fact is it doesn't remotely represent 

15 what the American people think about privacy. 

16 You know, if your social network only 

17 had the settings of public and only me, that was 

18 the only option, you know, people would say this 

19 is ridiculous. 

20 And I do think it sounds strange to say 

21 it, but we do have something to learn from the 

22 best practices of these social networks, in that 
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1 they very much see the world as a series of 

2 segments and they respect the fact that sometimes 

3 you want to share something with segment A and 

4 not segment B. And so I would say that's 

5 certainly valuable.

6 I don't have a good test about 

7 identifying a reasonable expectation of privacy. 

8 I'll just repeat myself in that I think we need 

9 to see that as a standard that can expand and 

10 contract.

11 MS. ANTON: If I could quickly add, 

12 after the Snowden leaks there's an anonymous 

13 search engine called DuckDuckGo and the number of 

14 people who started doing searches on that search 

15 engine increased, I think by over a hundred 

16 percent. So there's one way that you can watch 

17 people's actions and conduct.

18 MR. HINTZE: Just one very, very quick 

19 add-on to that. It's not a binary thing. You 

20 can't say that people say they care about privacy 

21 but they continue to use Facebook. 

22 You have to look deeper. You have to 
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1 look at about how they're using Facebook, whether 

2 they're using the privacy controls, how they're 

3 engaging in those services, because if you look 

4 deeper you see some pretty sophisticated choices 

5 that people are making in ways to protect their 

6 privacy that's not apparent on the fact that, oh, 

7 you're using a social network, you must not care 

8 about privacy. 

9 MS. WALD: I have a question. Between 

10 the two panels, the first panel and the second, I 

11 heard, I hope correctly, that there is some 

12 difference of opinion on a couple of things, or 

13 maybe slight. 

14 I think, Ms. Anton, you suggested in 

15 answer to a prior question of mine that you 

16 thought the government was indeed involved in 

17 trying to build privacy into the technological 

18 aspects of some of the programs. 

19 On the other hand, earlier I think you 

20 said that in threat modeling very little privacy 

21 considerations were going into that. 

22 Other people said that it wasn't 
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1 inevitable that the government would keep 

2 collecting more and more information, but I think 

3 I got that impression that maybe it seemed to be 

4 going that way from Mr. Felten on the earlier 

5 panel.

6 So my question is basically, very 

7 briefly, if there were one area of priority, if 

8 you were running the government's overall privacy 

9 protection that you would suggest they 

10 concentrate on and could perhaps improve privacy 

11 protection without endangering national security, 

12 what would it be? If you can do it very quickly.

13 MS. ANTON: I think that we really need 

14 to work more on privacy standards and not privacy 

15 standards globally, and also that aren't rigged 

16 in some way to help some government or sector of 

17 industry. I think that's the number one 

18 challenge right now.

19 MS. WALD: Other people? 

20 MR. BEDOYA: Yeah, I would say it's 

21 ending programs that involve the bulk collection 

22 of American's data.
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1 MS. WALD: I couldn't hear the end.

2 MR. BEDOYA: Ending programs that 

3 involve the bulk collection of American's data.

4 MS. WALD: Okay. Do you have in mind 

5 any except 215? 

6 MR. HINTZE: I didn't have the TSSI 

7 clearance so I don't know.

8 MR. DEMPSEY: Okay, Mr. Chairman?

9 MS. WALD: Wait a minute, there was 

10 somebody wanted --

11 MR. DEMPSEY: Oh, I'm sorry. Yes, 

12 please.

13 MR. NAHARI: One last thing, and I don't 

14 know if this is the elephant in the room. One 

15 thing I would put as an item priority is our 

16 systems and the technology are very much built as 

17 one way. So I would introduce a notion of 

18 revocation. 

19 So if something goes bad right now, if 

20 I'm releasing all of this information, there is 

21 no way for a user, for a citizen to go ahead and 

22 push a button somewhere and say revoke all the 
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1 rights that I gave to XYZ service providers and I 

2 want to go ahead and clear everything. 

3 So defining what that revocation means, 

4 what are the ramifications of that, and how to 

5 crystallize it as a requirement for the industry 

6 would go a long way for things that we could 

7 build.

8 MS. WALD: That would go primarily to 

9 industry, that wouldn't affect government. I 

10 mean if I gave the government some information 

11 under some program which I thought was going to 

12 benefit me and later on it turned out it was 

13 being used in a different way, would your 

14 revocation principle apply there?

15 MR. NAHARI: If I have the right to 

16 revoke whatever government had collected about me 

17 and I knew things that our government, in the 

18 possession of government and I was able to revoke 

19 that, perhaps that would be helpful.

20 MS. WALD: Thank you.

21 MR. DEMPSEY: So this concludes our 

22 second panel. It concludes our morning session. 
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1 We will reconvene at 1:15 with a panel of 

2 government privacy officers.

3 (Off the record.) 

4 MR. MEDINE: Good afternoon. The 

5 Privacy and Civil Liberties Oversight Board's 

6 meeting on defining privacy will continue with 

7 our afternoon session with government panelists 

8 moderated by a member, Beth Cook.

9 MS. COLLINS COOK: So welcome back to 

10 folks who were here earlier, or welcome to those 

11 who were not here. 

12 Just quick one piece of housekeeping, 

13 what we've noticed this morning is make sure, and 

14 Alex, this will be particularly relevant for you, 

15 make sure the microphone is actually the 

16 direction you are talking, so that even if you 

17 pull it in front of you but then turn to talk to 

18 us, make sure the microphone is picking up. They 

19 were having problems this morning and we've all 

20 been gently reminded as well.

21 All right, so this panel is about the 

22 privacy interests identified and addressed by 
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1 government privacy officials. 

2 Obviously in the counterterrorism 

3 context defining and expressly articulating 

4 individual privacy interests while balancing the 

5 needs of national security is an extremely 

6 challenging task. 

7 As we discussed a bit this morning, 

8 widely accepted privacy frameworks like the Fair 

9 Information Practice Principles or traditional 

10 privacy impact assessments may very well be 

11 intentioned with the necessity to protect 

12 information regarding the operation of a 

13 particular counterterrorism program. 

14 By the same token, some counterterrorism 

15 programs could be better served with greater 

16 transparency about what information is being 

17 collected, about the statutory authorities or the 

18 authorities pursuant to which programs are being 

19 operated, and about what protections the 

20 government utilizes to minimize the negative 

21 impacts on individuals' privacy. 

22 So the panel that we have assembled 
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1 today for this forum is, I think, uniquely 

2 situated to discuss these privacy issues that 

3 arise in the context of federal counterterrism 

4 programs.

5 These officials not only assess the 

6 privacy impacts of a full spectrum of 

7 counterterrorism programs they have also been 

8 pioneers, many of them, in the practice of 

9 working proactively within the agencies to ensure 

10 privacy and civil liberties concerns are taken 

11 into consideration from the beginning of 

12 programs. 

13 And if that were not enough of their 

14 duties, they also are learning to live with us 

15 and work with us. 

16 Joining me today are three individuals. 

17 Unfortunatly DHS was not able to make anyone 

18 available for this as it turned out. 

19 So we have three folks. They will have 

20 ten minutes, given that they have a little bit of 

21 extra time, few folks, but we will follow the 

22 same basic framework. 
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1 I will then ask a series of questions 

2 for a period of time and then invite my fellow 

3 panelists to submit questions as well. 

4 So leading us off is Alex Joel who is 

5 the Civil Liberties Protection Officer for the 

6 Office of the Director of National Intelligence.

7 Do you actually fit that on one card?

8 MR. JOEL: Yes, I do.

9 MS. COLLINS COOK: That's amazing.

10 So in that capacity he leads the ODNI's 

11 Civil Liberties and Privacy Office and he reports 

12 directly to the Director of National 

13 Intelligence. 

14 Prior to joining the government, and I 

15 think this is also relevant based on our other 

16 panels, Alex served as the privacy, technology 

17 and e-commerce attorney for Marriott 

18 International, where he helped establish and 

19 implement Marriott's global privacy compliance 

20 program, including the creation of Marriott's 

21 first privacy officer position.

22 So, Alex, did you want to kick us off?
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1 MR. JOEL: Yes, thank you. And I want 

2 to thank the Board for --

3 MS. COLLINS COOK: Oh, I'm sorry, 

4 there's a stop light function going on here, 

5 green, good to go, yellow, start wrapping up, 

6 red, stop, in the front row. 

7 MR. JOEL: Okay. I want to thank the 

8 Board for inviting us here to address the public 

9 in this very important hearing. 

10 And as you said, the Board does work 

11 very closely with us. We feel that the Board's 

12 role in providing both transparency and 

13 oversight, as well as advice to the intelligence 

14 community has been extremely valuable and is a 

15 critical part of how the intelligence community 

16 protects privacy and civil liberties. 

17 So I want to thank the Board for holding 

18 this hearing and for the Board's very diligent 

19 and careful efforts to exercise their statutory 

20 functions, which I think have been critically 

21 important.

22 This topic is, of course, one that 
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1 consumes all of us, not specifically how to 

2 define privacy, but how to apply protections 

3 required to protect privacy in the context of our 

4 activities and in particular in the context of 

5 counterterrorism activities. 

6 I'd like to just get to what I think of 

7 as the heart of the matter from an intelligence 

8 community perspective in any event, which is that 

9 we operate by necessity within a sphere of 

10 secrecy. 

11 We have to be able to maintain secrets 

12 in order to be effective. The more publicly 

13 transparant an intelligence service is, the more 

14 it informs adversaries of how the agencies are 

15 collecting information and the better able those 

16 adversaries are to avoid detection. 

17 So as I've said in the past, a fully 

18 transparent intelligence service is by definition 

19 an ineffective one.

20 The key for us then is how within the 

21 sphere of necessary secrecy do you make sure that 

22 the intelligence agencies are acting 
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1 appropriately, lawfully, and in a way that 

2 protects people's privacy and civil liberties 

3 consistent with the values of the nation. 

4 In the past what we have done, as you 

5 know, is focused on ensuring that we are 

6 providing full transparency to our oversight 

7 entities. And our oversight system is something 

8 that I would like to characterize as a system of 

9 many layers with many players. 

10 We have not only within each agency, 

11 offices of general counsel and offices of 

12 inspectors general, as well as newly created 

13 privacy and civil liberties offices, but outside 

14 of the agency we have entities like the 

15 Department of Justice, which is responsible on a 

16 government-wide basis for exercising some of 

17 these authorities and oversight controls.

18 We have of course newly created entities 

19 like the Privacy and Civil Liberties Oversight 

20 Board, perhaps not that new anymore, which again 

21 is designed to make sure that there is a secure 

22 place for information to be disclosed and 
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1 discussed so that the oversight institutions are 

2 satified that the activities being conducted are 

3 proper ones. 

4 Then of course we have Congress and the 

5 judiciary, both of which exercise robust 

6 oversight. And I would mention that, for 

7 example, the congressional oversight committees, 

8 which were established particularly after the 

9 Church Committee hearings in the 1970s to provide 

10 this granular level of oversight over 

11 intelligence activities, has been very effective 

12 in my view in providing careful oversight of what 

13 we do. 

14 So that's sort of the oversight part of 

15 the equation. I think what we have now more 

16 fully realized is the need to enhance 

17 transparency. 

18 So if you think of it, I mean I was just 

19 thinking about this before I started talking, 

20 which is always dangerous, but if you think of it 

21 as operating within a sphere of secrecy, one way 

22 is to make sure that the mechanisms, the rules 
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1 and oversight structure within that sphere are 

2 robust enough to make sure that privacy interests 

3 and civil liberties interests are being 

4 adequately protected. 

5 And then there's the other way of 

6 approaching this, which we're also focusing on 

7 doing, which is reducing that sphere. 

8 In other words, providing greater 

9 transparency into what goes on inside the 

10 intelligence agencies so that the public at large 

11 can get reassurance and can also provide input 

12 and feedback into how we conduct these 

13 activities.

14 I think if I could just continue along 

15 this theme, there are two aspects in particular 

16 of what goes on to regulate our activities that I 

17 think is of interest. One is the rules that we 

18 follow, and the other is the oversight framwork 

19 and mechanisms designed to make sure we're 

20 following those rules.

21 So I think on the former, what are the 

22 rules that we follow? We can and should provide 
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1 greater transperancy, but a lot of those rules 

2 are now currently being debated and discussed, 

3 and you can think of some of the reform 

4 mechanisms as attempts to modify those rules.

5 So you have the activity going on in 

6 Congress, for example, the USA Freedom Act and 

7 similar legislative initiatives. 

8 You have as part of that also the 

9 proposal to create an advocate of some kind, an 

10 adversarial mechanism for the Foreign 

11 Intelligence Surveillance Court. 

12 Here again in my view is an attempt to 

13 influence or affect what are the rules that the 

14 intelligence agencies are expected to follow. 

15 And then a different part of that 

16 question is what oversight mechanisms, what 

17 assurances do we have that the agencies are, in 

18 fact, following those rules. 

19 And you're part of that. I've already 

20 mentioned the congressional committees, the 

21 Foreign Intelligence Surveillance Court, and then 

22 all the layers within the executive branch itself 
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1 at the intelligence community and the Department 

2 of Justice level.

3 So I think, I hope that the public 

4 discussion has been shifting a bit from whether 

5 or not we're following the rules. I think what I 

6 perceived in the public discussion is a greater 

7 acceptance that we are in fact trying our best to 

8 follow the rules. We're not perfect and we make 

9 mistakes, but we're trying to follow those rules 

10 as best as we can. 

11 And now the discussion has been shifting 

12 to, well, what should those rules be? What are 

13 the rules, and what should those rules be? 

14 I think we can and must provide greater 

15 transparency into both sides of that equation, 

16 and we're working on that. 

17 I would also say that another thing that 

18 I know the Board has been pursuing which is the 

19 recommendation that the Board made in the 702 

20 report regarding efficacy, you know, to what 

21 extent are the counterterrorism programs and 

22 measures effective and to what extent do they 
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1 provide value is a key part, in my view, of the 

2 transparency equation as well.

3 We have to figure out ways to identify 

4 the specific value associated with particular 

5 programs and activities, and then be more 

6 transparent about that so that the American 

7 people can render a judgment, as well as everyone 

8 else, on the need or desirability for a 

9 particular kind of program. 

10 It is very difficult to do all this 

11 stuff and still maintain secrets. The 

12 intelligence communitity is not built for 

13 transparency. I've said this before, it's built 

14 for exactly the opposite, of course. 

15 We train, provide policies and systems 

16 and reminders to our workforce of the importance 

17 of maintaining secrets, you know, maintaining 

18 secret the sources and methods that the 

19 intelligence community uses to carry out its 

20 activities. And this is vital. I mean we have 

21 to do that and we're reminded of that need all 

22 the time.
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1 But at at the same time, we have to find 

2 ways to enhance transparency. It's going to 

3 involve some changes in culture, training, a look 

4 at policies and processes within the intellgence 

5 community and I know that you may want to ask 

6 questions about that, so I look forward to that 

7 discussion.

8 So thank you again. I appreciate it.

9 MS. COLLINS COOK: So turning now to 

10 Erika Brown Lee, she is the Chief Privacy and 

11 Civil Liberties Officer of the Department of 

12 Justice. In that capacity she is the principal 

13 advisor to the Attorney General on privacy and 

14 civial liberties matters affecting the 

15 department's missions and operations. 

16 And as part of the Office of Deputy 

17 Attorney General, Ms. Brown Lee oversees the 

18 department's privacy and civil liberties programs 

19 and initiatives implemented by department 

20 components and component privacy and civil 

21 liberties officials. 

22 She also heads the Office of Privacy and 
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1 Civil Liberties, which reviews and evaluates 

2 department programs and initiatives, and provides 

3 department-wide legal advice and guidance to 

4 ensure compliance with applicable privacy laws 

5 and policies, including the Privacy Act. Thank 

6 you for coming.

7 MS. BROWN LEE: Thank you, and thank you 

8 to the Board for inviting me here to talk about 

9 what is a very important topic.

10 You asked about private sector 

11 experience and other government experience, I 

12 also come from the Federal Trade Commission, 

13 which in particular the Division of Privacy and 

14 Identity Protection, which of course the Federal 

15 Trade Commission has a very different orientation 

16 toward the commercial side of privacy, but 

17 nonetheless an important perspective and an 

18 interesting one to bring to this position. 

19 But counterterrorism is a significant 

20 part of the department's mission. Since my 

21 colleagues on the dias today will be talking from 

22 more of an intelligence lens, I thought I would 
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1 orient my remarks more toward the department's 

2 efforts to fight terrorism from within the 

3 criminal law enforcement context. 

4 The department has an elaborate 

5 architecture that protects privacy in our 

6 counterterrorism work, and since I only have a 

7 few minutes I'll focus on the lead agency in 

8 those efforts, which is the FBI and focus in a 

9 little bit more on the efforts with their 

10 counterterrorism activities. 

11 But stepping back for a minute, of 

12 course as we know after 9/11, it was recognized 

13 that in order to address the current threat 

14 environment, FBI's functions needed to be 

15 expanded, but it was not intended that the 

16 expansions would come at a cost of civil 

17 liberties.

18 So in 2008, the department issued the 

19 Attorney General Guidelines for Domestic FBI 

20 Operations, the AGG-DOM, and later that year 

21 issued the DIOG, or the Domestic Investigations 

22 Operations Manuel. And combined, those two 
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1 documents provide significant guidance for FBI 

2 activities.

3 But what I wanted to talk about, and I 

4 know I don't have enough time to get too far into 

5 the weeds, is just to explain how privacy is sort 

6 of embedded throughout the stages of an 

7 investigation, from the initial phase throughout 

8 the process. 

9 And so, for example, one of the key 

10 tenants of both documents is the least intrusive 

11 method. So in other words, in any activity that 

12 the FBI engages, that's the baseline. 

13 But of course within the 

14 counterterrorism context, it's got to be 

15 calibrated against the threat to national 

16 security, in which case more intrusive methods 

17 would be used. 

18 But in terms of a little bit more detail 

19 from an operational context, when an FBI conducts 

20 an assessesment, for example, which necessarily, 

21 well not necesssarily, but oftentimes is 

22 proactive, that would involve, doesn't require a 
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1 factual predication, but it does require a 

2 clearly defined objective. 

3 And the least intrusive methods in that 

4 context would be even starting with publically 

5 available information, to voluntarily provided 

6 information, in that perspective.

7 And then moving up from there with 

8 regard to predicated investigations, which of 

9 course implies by title, there requires a factual 

10 predication to open that investigation, but that 

11 has to have supervisory approval. 

12 And both types of investigative 

13 activities, whether it's assessments or 

14 predicated investigations require or are, I 

15 should say, subject to oversight.

16 Alex mentioned DOJ oversight on the 

17 intelligence side, but also on the law 

18 enforcement side for counterterrorism, the 

19 department's National Security Division has 

20 oversight authority for those kinds of 

21 activities.

22 Now Beth mentioned and asked us to talk 
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1 about or think about how the FIPS apply, if 

2 you're looking for the acromyn, there's lots of 

3 them in the documents, but it's not actually in 

4 the AGG-DOM or the DIOG. However, they are 

5 embedded throughout really, the princples. 

6 If you think about, even from a 

7 transparency perspective, right, all that I'm 

8 discussing with regard to the DIOG, all 700 pages 

9 of it for a little light reading, for anyone 

10 who's interested it's on the web with certain 

11 redactions. 

12 But also we have privacy impact 

13 assessments that are available. And one that I 

14 wanted to just mention in particular regards the 

15 eGuardian system because that is a specific 

16 system or incident reporting system that is 

17 designed as a platform to share terrorism-related 

18 information across law enforcement, you know, 

19 federal, state, local, tribal, territorial 

20 jurisdictions.

21 So eGaurdian, I don't have time to go 

22 into much detail about it, but it has an entire 
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1 architecture of privacy protections governing how 

2 information comes into eGuardian, how it's shared 

3 across those entities, how it's stored and how 

4 it's retained.

5 Individual participation as a FIPS 

6 principle, obviously that's more of a challenge 

7 in a law enforcement context. It's not realistic 

8 to be able to obtain individual consent in order 

9 to pursue criminal investigations. 

10 But nonetheless, the Privacy Act 

11 provides some measure of review in the sense that 

12 if access or amendment to records is denied, 

13 there is judicial review of an agency's decision, 

14 and subject to court order, records may be 

15 amended or access may be granted.

16 On the minimization side, I mentioned 

17 the least intrusive means already with the DIOG. 

18 There's also a prescriptive measure in the DIOG 

19 with regard to evidence collected, that if the 

20 evidence collected through an assessment or 

21 through a predicated investigation has no 

22 forseeable future evidentiary or intelligence 
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1 value, it should be returned and destroyed, and 

2 then marked in the file in term of the 

3 disposition of that piece of evidence. 

4 Otherwise, information is retained 

5 according to the schedule set by NARA, the 

6 National Archives and Records Administration, and 

7 approved, through which the Department of Justice 

8 would seek approval for. 

9 With regard to use, I think that's also 

10 a challenge. On the criminal side of course 

11 willful disclosures of protected information 

12 under the Privacy Act are not something that any 

13 agency can exempt themselves from. 

14 And to the extent that information is 

15 released that's not subject to a routine use or 

16 other permitted disclosure, and of course, you 

17 know, routine uses are subject to a compatibility 

18 standard that tracks the FIPS language. 

19 If the information is disclosed or even 

20 shared in violaton of that, that's potentially a 

21 wrongful disclosure subject to not only civil 

22 damages but criminal penalties. 
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1 And then in terms of accountability, I 

2 mentioned oversight from the National Security 

3 Division, but also the FBI has the National 

4 Security Law Branch, which conducts national 

5 security reviews. 

6 And that's a significant review 

7 process in that they go out to all of the field 

8 offices and review the investigative activities I 

9 mentioned, the assessments, the predicated 

10 investigations and look to see whether, in fact, 

11 superviseory approval was obtained, whether, in 

12 fact, there was a clearly defined objective for 

13 any assessment, and it's written up into a 

14 report. 

15 That report actually comes through FBI 

16 channels of course, but then also comes for 

17 review by the Chief Privacy and Civil Liberties 

18 Officer. And I look at those, obviously, through 

19 a privacy and civil liberties lens. 

20 So as Alex was mentioning, there are 

21 lots of layers that are applicable.

22 I know I don't have much time remaining. 
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1 But in conclusion, I guess I would just like to 

2 leave you with a couple of take-aways.

3 One is that FIPS, quite to the contrary 

4 of certain statements is not dead, it's just 

5 embedded.

6 And I would also say that the processes 

7 can always be improved. Certainly I work with 

8 the component, each component. There are over 40 

9 components in DOJ, but each component has a 

10 Senior Component Official for privacy and I host 

11 regular meetings. 

12 In fact, we're having a privacy forum 

13 next week that will cover privacy-related 

14 activities focusing on law enforcement, but other 

15 compoments as well, activities, common privacy 

16 issues across components. It is internal though 

17 so none of you are actually invited unless you 

18 happen to get a job by Monday at the DOJ.

19 But that's also something that is a way 

20 to improve. And I would also say that while 

21 privacy impact assessments are very important and 

22 a critical part of our program because they're 
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1 sort of this tangible proof that we actually 

2 evaluate privacy, that we mitigate the risks, 

3 that we take into account security and 

4 accountability, they really only form a part of 

5 the architecture for the Department of Justice's 

6 privacy program.

7 So, and I welcome your comments.

8 MS. COLLINS COOK: Thank you, Erika, for 

9 a nice education about the FBI's operations, the 

10 FBI in particular.

11 So Becky Richards is the National 

12 Security Agency's Civil Liberties and Privacy 

13 Officer. In this, I think, relatively new role, 

14 I think it's fair to say, she provides expert 

15 advice to the Director of NSA on all issues 

16 pertaining to privacy and civil liberties 

17 protections, and she conducts oversight of NSA's 

18 civil liberties and privacy-related activities. 

19 She also develops measures, which I hope 

20 she will talk about, to further strengthen NSA's 

21 privacy protections. 

22 Prior to joining the National Security 
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1 Agency, she worked as the Senior Director for 

2 Privacy Compliance at the Department of Homeland 

3 Security.

4 MS. RICHARDS: Thank you, and thank you 

5 for hosting us. I am very honored to have been 

6 selected to be the first NSA's Civil Liberties 

7 and Privacy Officer. 

8 This is an exciting time to be a member 

9 of the civil liberties and privacy community. 

10 Our community is growing and evolving and will 

11 help inform the debate as the nation reshapes its 

12 expectations for and limitations on the 

13 intelligence community activities.

14 Changes in the nature of the threat to 

15 our national security, alongside rapid advances 

16 of technology, as was discussed earlier, make my 

17 job both interesting and challenging.

18 Technology provides us with both 

19 opportunities and challenges, but ultimately we 

20 must guide and shape its use to ensure the 

21 fundamental rights we hold dear as a nation are 

22 maintained.
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1 Today I'd like to take a little time to 

2 describe NSA's civil liberties and privacy 

3 programs, both in the past, present, and a few 

4 thoughts on the future. 

5 Part of the NSA's mission is to obtain 

6 foreign intelligence worth knowing derived from 

7 foreign communications in response to 

8 requirements and priorities validated and levied 

9 upon us by the executive branch.

10 One such priority is counterterrorism, 

11 but there are other threats to the nation, such 

12 as the spread of nuclear, chemical or biological 

13 weapons, or cyberattacks.

14 NSA also works directly with and 

15 supports our troops and allies by providing 

16 foreign intelligence for military operations 

17 abroad.

18 As we consider NSA's civil liberties and 

19 privacy programs over the past 62 years, it's 

20 important to think about how the threat, 

21 technological and sociatial landscape in which 

22 NSA conducts itself signant mission has changed.
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1 First, the threat has changed. NSA 

2 previously operated in a cold war era when the 

3 focus of collection for foreign intelligence was 

4 directed at nation states, structured military 

5 units, and foreign intelligence services. 

6 While threats remain from nation states, 

7 they now also come from non-state actors, which 

8 require NSA to look at more, smaller and 

9 decentralized targets to protect the nation.

10 The technology has changed. NSA again 

11 previously operated in an environment where the 

12 communications between foreign intelligence 

13 targets were frequently conducted over isolated, 

14 government-owned and operated communication 

15 channels and equipment. 

16 Now foreign target communications are 

17 interspersed with ordinary commerical and 

18 personal communications. 

19 Additionally, the sheer volume and 

20 ability to analyze and manipulate big data, which 

21 has occurred as a result of significant advances 

22 in information technology, can expose information 
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1 of a personal nature that may not have been 

2 previosly discoverable and may not be of any 

3 interest.

4 Third, how society thinks about civil 

5 liberties and privacy has changed. We've come a 

6 long and positive way in thinking about what 

7 ought to be private. 

8 Personally identifiable information was 

9 not a mainstream issue 25 years ago. For 

10 example, Social Security numbers were routinely 

11 put on student ID cards and there was no thought 

12 of HIPAA. 

13 So with that I'd like to give a little 

14 historical perspective. NSA's civil liberties 

15 and privacy protections have historically been 

16 driven primarily by the Fourth Amendment 

17 analysis, which is also reflected in NSA's 

18 authorities, Executive Order 12333 Foreign 

19 Intelligence Surveillance Act, or FISA.

20 This analysis framed NSA's protection 

21 program by asking where and how the data was 

22 collected, i.e., usually overseas, and the status 
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1 of the individual or entity being targeted, i.e., 

2 is it a U.S. person or not.

3 NSA has consistentily conducted 

4 extensive legal analysis as it considers new 

5 types of collection answering these types of 

6 questions. It has built a strong compliance 

7 program based on these, with compliance 

8 activities embedded in our technologies and 

9 systems.

10 As I have learned more about NSA and its 

11 compliance regime, it became clear while this is 

12 certainly one way to address privacy concerns, it 

13 is somewhat different from how privacy concerns 

14 are addressed outside of NSA. 

15 Over the last 15 years Congress has 

16 passed a variety of laws to protect privacy in 

17 other parts of the government and the commercial 

18 sector. These policies and laws focus more on 

19 the nature and content of the data and how it is 

20 used, not where it was collected or the 

21 citizenship of the individual. 

22 I believe we have an opportunity to 
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1 bring together NSA's current civil liberties and 

2 privacy analysis with a broader approach to 

3 privacy and civil liberties. 

4 This new approach also supports the 

5 President's PPD-28 mandate to recognize that our 

6 signals intelligence activities must take into 

7 account that all persons should be treated with 

8 diginity and respect, regardless of their 

9 nationality and wherever they might reside, and 

10 that all person's have legitimate privacy 

11 interests in handling their personal information.

12 To address a broader set of civil 

13 liberties and privacy interests, I'm testing a 

14 civil liberties and privacy assessment process 

15 that expands NSA's views to include 

16 considerations of frameworks the private sector 

17 and nonintelligence elements of the government 

18 use to assess civil liberties and privacy. 

19 For example, for the first time in its 

20 history, NSA is using the Fair Information 

21 Practices Principles, or FIPS, as a framework for 

22 considering civil liberties and privacy risks.
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1 The FIPS are one framework through which 

2 organizations can analyze the protections they 

3 have in place for personal information. 

4 While traditional NSA civil liberties 

5 and privacy questions center on citizenship and 

6 location of foreign intelligence targets, as well 

7 as collection techniques, FIPS related questions 

8 boil down to follow the data. 

9 Data-centric perspectives mean privacy 

10 officials ask a different set of questions. What 

11 is the data being collected and how will it be 

12 used? 

13 As such, we've designed an initial 

14 standarized template and during the next year 

15 we'll refine the questions and process to ensure 

16 we're building a repeatable, meaningful and 

17 helpful process to identify and mitigate civil 

18 liberties and privacy risks.

19 A critical part of the civil liberties 

20 and privacy assessment process is to make sure 

21 we're not merely checking off boxes, but 

22 fundamentally weighing the risks associated with 
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1 the activity to form a holistic value 

2 proposition. 

3 In essence, we're asking should NSA 

4 conduct a given activity, given its civil 

5 liberties and privacy risks? 

6 As part of the assessment process NSA is 

7 documenting both standard protections, such as 

8 minimization and control on who has access, as 

9 well as any specialized tools designed to protect 

10 civil liberties and privacy.

11 Much like privacy analysis performed in 

12 the private sector and other parts of the 

13 government, we're using the FIPS as the basis for 

14 analyzing what existing protections are in place.

15 As we look to the future, I'd like to 

16 spend a little bit of time talking about blending 

17 the art and science of privacy.

18 Historically privacy tends to be a bit 

19 of an art form. Several of us stand around and 

20 think about how we're going to do the analysis. 

21 This can be difficult when we're beginning to 

22 think about big data and the complexity that was 
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1 being discussed this morning.

2 NSA is fundamentally a technology3 centric organization. We have and will continue 

4 to contribute to advancing the discussion and 

5 research of protecting civil liberties and 

6 privacy. 

7 Today the science of privacy has made 

8 notable strides that include developing 

9 technology and tools that promote privacy, such 

10 as unique encryption capablilities, digital 

11 rights management and trustworthy computing.

12 Great work in private sector and 

13 academeia is also being developed on coding 

14 privacy policies, such that technology supports 

15 all specific uses. 

16 But civil liberties and privacy 

17 protections need to blend the art and science of 

18 privacy if we're going to harness the potential 

19 of technology and incorporate our core values as 

20 a nation into this era of big data. 

21 So despite significant progress in 

22 privacy technology, basic privacy of principles 
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1 found in a strong scientific basis, have largely 

2 proven elusive. 

3 If we can better understand what 

4 constitues personal information and how such 

5 information is used, we believe it will be 

6 possible to determine whether we can develop more 

7 practical approaches to evaluating the inherent 

8 risk of privacy to the individual.

9 To that end, our initial thoughts are to 

10 develop five sequential building blocks and to 

11 introduce the concept of some very difficult math 

12 into what is otherwise a very nice liberal arts 

13 discussion of privacy. 

14 The first one is to catagorize personal 

15 information. We would like to determine if it's 

16 possible to identify and catagorize different 

17 types of personal information and what that risk 

18 is to privacy.

19 Now we've heard different discussions 

20 today, but we want to push folks to think about 

21 is certain types of data more risky to privacy, 

22 say likehealth data, than other information, say 
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1 your address, and can we think about those risks. 

2 If we can do that, then next we would 

3 like to determine if it is possible to identify 

4 and catagorize different types of use.

5 If we take both of these together, it's 

6 possible to develop a catagorization of both 

7 personal information and uses of the personal 

8 information, it should be possible to develop a 

9 scientific process to assess risk.

10 This process could evaluate the risk of 

11 the use of individual types of personal 

12 information for different uses, as well as 

13 aggregated uses of personal information.

14 Now with these three building blocks 

15 being more of the scientific aspect, I would now 

16 suggest we would move to an art form that looks 

17 at how we build that to identify what needs to 

18 have additional privacy impact analysis 

19 conducted so that we're looking at that across 

20 the board.

21 With all four of these together then we 

22 would look to see if we could build a responsible 
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1 use framwork that holds data collectors and users 

2 accountable for how they manage data and any harm 

3 it causes.

4 Building a technical means based on 

5 principled scientific methodologies to support 

6 the identification of civil liberties and privacy 

7 risks can help us better protect civil liberties 

8 and privacy in a fluid world of big data.

9 Success is dependent upon input from a 

10 variety of disciplines ranging from 

11 technologists, social scientists, privacy and 

12 civil liberties experts, ethicists, attorneys and 

13 computer scientists, to name a few.

14 We would welcome the opportunity to 

15 discuss this in more detail and greater technical 

16 depths at a later date.

17 With that, I thank you for the 

18 opportunity and I'm happy to answer what I'm sure 

19 are a couple of questions.

20 MS. COLLINS COOK: Thank you all for 

21 your opening remarks. 

22 Becky, I wanted to stick with you for 
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1 just a second. When we go and meet with y'all 

2 and when we talk to y'all, there is frequently 

3 someone from the general counsel's office, 

4 someone from the compliance office, someone from 

5 your office. 

6 What are you doing that is different 

7 than the general counsel's office and a 

8 compliance shop? 

9 MS. RICHARDS: That's a great question. 

10 So the civil liberties and privacy office at NSA 

11 is the focal point for questions surrounding 

12 civil liberties and privacy, and it's been 

13 brought to a senior leadership position at NSA in 

14 order to focus on those efforts. 

15 So generally speaking, our general 

16 counsel will answer the legal question, is this 

17 legal permissable? And they will often then work 

18 with compliance for, what are the rules? 

19 But we haven't had a person asking some 

20 of these more difficult questions of, should we 

21 be doing this? 

22 Now frequently our oversight folks, 
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1 whether it's ODNI and DOJ, were playing that 

2 role. And so I don't want you to take away the 

3 idea that those questions weren't asked.

4 But it's really important to have that 

5 type of a role inside the building where you are 

6 working with the operators and the technologists 

7 and can spend a great deal of time understanding 

8 what we're trying to do and bring to bear those 

9 questions.

10 MS. COLLINS COOK: Erika, a similar 

11 question for you. FBI, for example, has its own 

12 privacy officer, has its own general counsel, has 

13 its own compliance shop. 

14 What is your relationship and what is 

15 your ability to provide recommendations or to 

16 actually impose requirements on the FBI? 

17 MS. BROWN LEE: So also a very 

18 interesting question. My role and position is 

19 department-wide, so of course I have oversight 

20 over the compliance for DOJ as a whole. 

21 Each component, as I mentioned, has a 

22 senior official for privacy, but in addition has 
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1 general counsel's office that has significant 

2 footprints in privacy. So at FBI they have their 

3 privacy and civil liberties unit that's headed by 

4 a chief. 

5 I work quite significantly with that 

6 person in that office to specifically address 

7 compliance issues, to specifically address 

8 privacy initatives that I feel are important for 

9 the bureau to consider. 

10 Ultimately it is somewhat of a reporting 

11 structure. In other words, if there is a 

12 recommendation, or a particular policy or 

13 statutory obligation, FBI has the responsibility 

14 to comply. 

15 But part of what my job is, is to 

16 advocate and to make sure that that is occurring 

17 on a regular basis and that looking for ways that 

18 I can improve the process, looking for ways, for 

19 example, I talked about privacy impact 

20 assessments. Some of that is, if you look at the 

21 E-Government Act, it's written fairly broadly. 

22 I take, you know, a particularly broad 
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1 view of what I think should have assessments as 

2 part of compliance there. And so that's what I 

3 work in particular with the FBI on.

4 MS. COLLINS COOK: So Alex, a related 

5 but different question for you. How do you 

6 ensure that you have access, do you ensure that 

7 you have access to what various agencies are 

8 doing, or do you find yourself periodically 

9 reading about new programs, alleged new programs 

10 on the front page of the New York Times?

11 MR. JOEL: I'm surprised by that 

12 question. Information sharing is perfect 

13 everywhere in government.

14 MS. COLLINS COOK: I'm also seeking free 

15 advice because obviously one of our biggest 

16 challenges is going to be knowing what the 

17 agencies are doing. You can't conduct oversight 

18 of something you don't know is happening.

19 MR. JOEL: Right. I think that it's a 

20 major challenge for all of us. I know that, as 

21 you said, it's something that you're focused on. 

22 I know that it's a challenge for everybody. 
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1 It's a matter of, first of all, 

2 understanding the information flows within your 

3 own agency and trying to put in place markers for 

4 where it's important for you to be consulted.

5 The main way that I have just 

6 practically done it, since I've been doing this 

7 for about a decade now and when I first started, 

8 you know, it was just me and then we built a 

9 small staff over time, has been to form the 

10 trusted relationships inside the intelligence 

11 community and to make sure that the people that 

12 I'm working with and that are in positions of 

13 influence and authority to make decisions on 

14 programs and activities, understand the 

15 importance of consulting with civil liberties and 

16 privacy professional. 

17 In my own personal experience working 

18 within the intelligence communitity has been that 

19 when I first joined I was very pleasantly 

20 surprised that people were so focused on 

21 compliance and protecting privacy and civil 

22 liberties, doing the right thing, following the 
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1 right directives, and even when they might feel 

2 legally permitted to do something, they still 

3 gave voice to their own doubts as to whether they 

4 should be doing it.

5 And so I did not personally experience 

6 an uphill battle in trying to pursuade 

7 intelligence officers, hey, it's important for 

8 you to pay attention to civil liberties and 

9 privacy. 

10 In fact, it was sort of the opposite 

11 where many people felt that they were already 

12 doing that, and that it was their job to focus on 

13 that. 

14 For example, you mentioned Office of 

15 General Counsel. I was at an office of general 

16 counsel before coming to this job and we 

17 certainly felt when I was there that that was 

18 part of our job. We needed to look out for 

19 privacy and civil liberties, and not just what 

20 the law allowed, but what was the underlying 

21 intent and what should we be doing in that light.

22 So I certainly didn't want to take away 
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1 that sense of responsibility from anybody inside 

2 the intelligence community. 

3 My approach had always been, it's all of 

4 our jobs, it's part of our oath to support and 

5 defend the Constitution. There are offices that 

6 are particularly focused on that, Office of 

7 General Counsel, Office of Inspector General. 

8 There are intelligence oversight offices, as you 

9 guys have learned, that are. 

10 Now we're creating these civil liberties 

11 and privacy offices and I do think we add value 

12 because I think it is our full-time job to focus 

13 on civil liberties and privacy, so we bring 

14 focus, we bring an external perspective, and we 

15 have specific expertise, and training and 

16 experience that we can bring to bear, and then we 

17 can become a voice, as Erika said, an internal 

18 advocate for civil liberties and privacy. 

19 But I mean I think different agencies 

20 will find different ways of doing it. The ODNI 

21 is a fairly small organization, and the ODNI 

22 itself has mechanisms for understanding what's 
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1 going on across the intelligence community. So 

2 when a particular program or activity bubbles up 

3 to the point of a decision, either it comes 

4 automatically through my office or somebody will 

5 understand that I need to see it and route it to 

6 me.

7 MS. COLLINS COOK: So a follow-up, 

8 particularly to you, Alex, and Erika, both of you 

9 have fairly small staffs considering the breadth 

10 of your responsibilities, and we talked a lot 

11 this morning about the increasing technological 

12 complexity of what you are assessing.

13 Do you have the technological resources 

14 to understand what systems are actually doing? 

15 And I think that is both in terms of assessing on 

16 the front-end whether systems or programs should 

17 go live, or to the extent that there are 

18 restrictions, for example, if the FISA Court puts 

19 a restriction in place on a particular program, 

20 ensuring that those restricions are actually 

21 functioning.

22 MS. BROWN LEE: So I think that's a good 
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1 point. So but as I mentioned earlier, oversight, 

2 there are sort of a variety of roles in the 

3 department that have oversight, particularly with 

4 regard to counterterrorism. 

5 But my office is fairly small in the 

6 sense that given the large footprint of the 

7 Department of Justice, but they work incredibly 

8 hard and diligently with all of the components to 

9 ensure compliance. 

10 We rely quite a bit on internal 

11 component work that is done to produce 

12 information about what the privacy compliance is, 

13 and then also with regard to auditing and making 

14 sure that the privacy activities are actually 

15 effective.

16 But I would also say that some of the 

17 oversight, just to sort of again stress that, 

18 some of the oversight isn't just through my 

19 office, it's National Security Division, and FBI 

20 also has their branch, so we work very 

21 collaboratively.

22 And like Alex, I have found that within 
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1 the department there are a lot of people who care 

2 very deeply about these issues. It's not 

3 specifically in a privacy role as a title, but 

4 they have oversight and I think meaningful 

5 insight as to how the activities should consider 

6 and be consistent with privacy initiatives.

7 But, you know, it is something that I 

8 take into account and that's part of the reason 

9 why we have these internal conferences and 

10 whatnot that I'm trying to do to build upon that. 

11 MS. COLLINS COOK: And Alex, what do you 

12 do to make sure, the old adage is trust but 

13 verify, what do you do to make sure you actually 

14 understand the programs and the systems? 

15 MR. JOEL: Right. So it's a variety of 

16 things. One is, although I am not personally a 

17 technologist, I have been dealing with technology 

18 law, and legal issues and privacy issues 

19 associated with technology for much of my 

20 professional career.

21 So when I was at Marriott, I was the 

22 privacy, commerce and IT lawyer there. And then 
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1 before that I was at a law firm in downtown D.C. 

2 focused on large scale technology transactions. 

3 That doesn't make me a specialist in 

4 technology, but it does enable me to ask the 

5 right questions and make sure that the 

6 information is explained to me appropriately.

7 I don't have the staff resources to 

8 engage a full-time technologist. I think that 

9 would be helpful. I do think that you have to be 

10 a little bit careful with that because what you 

11 really want in that sense is a technology 

12 generalist. 

13 There are so many different aspects to 

14 to technology, as you know. I mean that's just a 

15 word that almost lacks meaning these days because 

16 we use it so frequently. 

17 But what NSA does for one particular 

18 type of activity will differ significantly from 

19 what FBI does, will differ significantly from 

20 what all agencies do in terms of database 

21 management.

22 So you've got database issues, you have 
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1 surveillance technologies, understanding 

2 communications technologies, understanding all 

3 kinds of different aspects to that issue.

4 And then of course the engineers and 

5 technologists, as we know, speak a different 

6 language from lawyers and so sometimes it's hard 

7 for everyone to speak to each other.

8 So what I have been doing is making sure 

9 that the information is clearly presented, that I 

10 see the documentation, that I personally 

11 understand it, that I trust the people who are 

12 providing me that information are giving me a 

13 complete picture, and then we also leverage 

14 technical experts in the particular field that we 

15 have access to within ODNI or through the agency. 

16 So if something comes up that we don't quite 

17 understand, we can reach out to somebody to have 

18 them help us understand it.

19 I think with a larger staff I would try 

20 to have more full-time technical expertise. 

21 MS. COLLINS COOK: Becky, you had 

22 mentioned that you've got a couple of pilot 
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1 experiments going and you mentioned also new 

2 technologies that may or may not be available. 

3 How are you working with the private 

4 sector to leverage what great thinking is going 

5 on, and is privacy a part of the procurement 

6 process, for example? And has consideration been 

7 given to that, that if we really want privacy to 

8 be from the ground up, should it be one of the 

9 procurement factors?

10 MS. RICHARDS: I'll start with the 

11 procurement. We actually started with the 

12 theory on procurement because in part that's how 

13 we were doing things at DHS. 

14 But it turns out NSA is a technology 

15 company that has a huge research portion of it 

16 and it also has a huge technology division. So 

17 it's two different parts.

18 So I actually have a technical director 

19 on staff who's here, Dave Marcos, and he and I 

20 have been working through sort of how do we think 

21 about the tech, how do we look at both what's out 

22 in the world, and so we're actually working with 
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1 several different groups within NSA to do an 

2 initial review of what is out there right now. 

3 And they're conducting that right now so 

4 we can get a sense of both from a policy and a 

5 technology perspective what's going on, as 

6 opposed to just things that we may know just, you 

7 know, from knowing different people, whether it's 

8 activities going on at MIT or Carnegie Mellon, 

9 you know, to make sure we had a broad breadth of 

10 understanding of what was the type of research 

11 going on.

12 So they're doing that. We're working on 

13 that right now, and then we're working with our 

14 research folks and trying to just leverage all of 

15 those things.

16 The procurment process is not really 

17 helping this happen best at NSA. And I think 

18 that that's, you know, each agency has its own 

19 culture and its own aspects. And so a lot of 

20 what I've been doing is taking the learning and 

21 sort of shifting it to make sure that building 

22 the program within NSA works for how NSA works. 
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1 And so that means that our privacy 

2 program is going to look a little bit different 

3 than FBI's or others. But it's based on sort of 

4 how the organization functions and where those 

5 key decisions are being made. So we're working 

6 through that.

7 But it turns out procurement really 

8 isn't really isn't quite the right place. So 

9 we're looking through in terms of both the 

10 technology, and the research director and others 

11 to make sure we understand where those touch 

12 points are. And that's a lot of why we're beta 

13 testing the processes.

14 MS. COLLINS COOK: So I think I have 

15 time for one last question before I turn it over 

16 to my fellow Board members.

17 Alex, this one's for you. You 

18 explicitly pointed to congressional oversight as 

19 one of the things that the American people should 

20 be aware of, that this happening, it's robust, 

21 it's real. 

22 A previous panelist pointed out that 
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1 there is potentially one significant flaw or 

2 challange with congressional oversight, and 

3 that's the lack of cleared staff. 

4 What has your perception been? Has 

5 Congress struck the right -- yes, I'm going to a 

6 ask you to opine on Congress -- whether 

7 consideration should be given to broadening the 

8 range of individuals? 

9 I think there's some comfort level with, 

10 I think someone called it delegated oversight 

11 within the Congress. But when some significant 

12 majority of decision-makers in a representative 

13 democracy don't have cleared staff, how is the 

14 oversight nonetheless sufficiently robust? 

15 MR. JOEL: So the intelligence oversight 

16 committees have very substantial cleared staff. 

17 And they of course have secure compartmented 

18 information. We have SCIFs in which to review 

19 all the classified information. And we have 

20 many, many meetings, briefings and reports with 

21 our oversight committees.

22 I guess my first response as a matter of 
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1 principle, yes, Congress should have the degree 

2 of staff cleared it needs in order to assist it 

3 to perform its oversight functions.

4 I think that intelligence community 

5 assumption had been that by clearing the staff of 

6 the oversight committees that that function was 

7 being fulfilled.

8 I think some staff members are also 

9 cleared from some of the other committees. I 

10 don't have all of that information in front of me 

11 but I believe judicary has cleared staffers, 

12 etcetera. 

13 Whether or not that's enough staff to be 

14 cleared, I don't know. I think Congress, from my 

15 personal perspective, it would be helpful if 

16 Congress figured out for itself which committees 

17 are performing which function and which staff 

18 members need to be cleared in order to oversee 

19 our activities and then we can assess it. 

20 But I would certainly support a desire 

21 to make sure that there are enough cleared staff 

22 to perform oversight, absolutely. 
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1 MS. COLLINS COOK: So transiting to the 

2 member questions and while this is happening, 

3 just a reminder there are folks with cards, if 

4 you have questions that you'd like to submit from 

5 the public. 

6 And to keep everyone on their toes, this 

7 time I'm going to start with Pat.

8 MS. WALD: Okay, you may be sorry about 

9 that choice. 

10 MS. COLLINS COOK: I might not be, they 

11 might be.

12 MS. WALD: This is somewhat of a loaded 

13 question, but it's one that's sort of in the back 

14 of so much of the work we have done and will 

15 continue to do.

16 You know, I laud all of Becky's 

17 attempts, and your attempts to inject, Erika, 

18 your attempts to inject privacy into all of the 

19 various phases of intelligence. 

20 But drawing upon what some of the people 

21 in the first panel said this morning, let me just 

22 pose a question that, for instance, several of 
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1 the panel members thought collection was a 

2 primary focus of trying to enhance privacy 

3 interests by limiting collection somewhat, and 

4 leaving apart any debate about whether or not 

5 collection by itself can be an injury to privacy, 

6 I guess, and that's collection. 

7 Also when you get, another expert talked 

8 about the risks to privacy from aggregating data. 

9 And we found out, for instance, in the 702 report 

10 we did, when you got to the retention of data the 

11 analysts might look at it and say, well, I don't 

12 see any foreign intelligence purpose to this 

13 piece of data if it came from an innocent person 

14 who's not the target, but it's conceivable there 

15 might be one down the line or some other person I 

16 don't know about, the agency, so therefore, I've 

17 got to bend to make sure that it's secured. 

18 So it seems to me one of the basic 

19 problems here will be, what's the tipping point? 

20 In other words, assuming good faith on both 

21 sides, there really is a national security 

22 interest when you have to make a choice between 
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1 privacy and national security, but the real 

2 question is, how much and at what point? 

3 In other words, when we were doing 215 

4 we were told many times we need a big haystack in 

5 order to find the needle, and the bigger the 

6 haystack, the more likely we are to find the 

7 needle.

8 But of course a policy judgment has to 

9 be made at some point. At this point, yes, we're 

10 going to lose some national security things but 

11 privacy is more important. 

12 I guess I want to know what your 

13 thoughts are about how that decision, which is a 

14 basic policy decision, but it seems to come up in 

15 every program that we look at, you know, how is 

16 it made or how it should be made, even at the 

17 most general level. You can all take a --

18 MR. JOEL: Okay, so I'll start. I'll 

19 offer some general observations.

20 MS. WADE: Yes.

21 MR. JOEL: So I think on the collection 

22 and use and retention point, I would say that 
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1 it's very important to look up each phase of 

2 that. And that's, in fact, how the intelligence 

3 community structures its determinations in many 

4 ways. It's collection, then there's retention, 

5 and then there's dissimanation. 

6 And on the collection point --

7 MS. WALD: And aggregation.

8 MR. JOEL: Right. And then of course 

9 when you aggregate data, you create additional 

10 risks.

11 So there's no question that if your 

12 concern is to protect privacy, the better way to 

13 do it, and you're worried about what the 

14 government's going to do with your data, it's 

15 always better for the government not to have the 

16 data. That's the best protection.

17 So if the government doesn't have the 

18 data, there is no risk to privacy from the 

19 government because they don't actually have it. 

20 So that's why I think it's appropriate of course 

21 to focus on collection. 

22 Once a determination is made that the 
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1 government really needs this data in order to 

2 carry out an important function, then you're 

3 shifting to retention. And so there are --

4 MS. WALD: Let me just interrupt you. 

5 I'm sorry to do this.

6 MR. JOEL: Okay.

7 MS. WALD: It's an old habit of mine 

8 leftover from -- 

9 MR. JOEL: Yes, your Honor.

10 MS. WALD: When you say, really needs, 

11 that's where the rubber hits the road.

12 MR. JOEL: Right.

13 MS. WALD: Because, sure, it's going to 

14 be useful. So where the line is between 

15 something which genuinely will be useful to you 

16 but will be more of a privacy risk, and the thing 

17 of, this is really necessary, because. And we 

18 all know it's going to be drawn differently in 

19 different case situations. 

20 But that's what it always seems to sort 

21 of come down to, and I'm wondering do you have 

22 any thoughts about how that, which is a policy --
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1 MR. JOEL: So this is where, and I know 

2 Becky, but before you used the term tipping 

3 point, which I think is a very helpful term, and 

4 sometimes people think of this as a balance or as 

5 a scale. 

6 The way that I think of the balance 

7 metaphor as it might apply here is not that 

8 you're saying, well, that tips it over here so 

9 therefore we're going to do it, that tips it over 

10 here therefore we're not going to do it. 

11 Although to some extent, of course, that happens.

12 The way that I think of it is that if 

13 you're going to do something new, a new or 

14 different collection program, you ask the 

15 following questions, A, is it lawful? Of course 

16 it has to be lawful. Is it justified? What is 

17 the purpose? You know, going to sort of a FIPS 

18 analysis, what is the purpose for it? Is this 

19 collection focused on a valid purpose that we 

20 feel should be pursued and is it important to be 

21 pursued, whatever the phrasing should be? 

22 And is your activity tailored to that 
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1 purpose? Are you doing something? Are there 

2 less intrusive ways of doing it? Is this the 

3 appropriate way to go about doing it in terms of 

4 obtaining this information? 

5 And then what are the risks to that? 

6 Sort of now going to the other side of the scale. 

7 And how do you guard against those risks? How do 

8 you mitigate those risks?

9 And this is the way that I've always 

10 thought of it. You know, it actually fits into 

11 some FIPS kind of models. It also fits into some 

12 privacy and assessment kind of models.

13 But if you look at that overall picture 

14 it then helps inform you, either the art or 

15 science side, I don't know, Becky can tell us 

16 which one that is.

17 It helps inform the decision about 

18 whether this is the right thing to do. And I 

19 think you have to look at that to tell.

20 So if you're just going to do one 

21 program, well, it's lawful and we think we need 

22 it. But now you can't figure out, there are 
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1 major risks, but you can't figure out how to 

2 adequatly mitigate those risks, then that will 

3 tell you one thing about the overall risk of 

4 doing that activity.

5 MS. COLLINS COOK: Alex, if we could.

6 MR. JOEL: Oh, I'm sorry.

7 MS. COLLINS COOK: That's all right. 

8 And Becky, did you have something specific you 

9 wanted to say in response to this question?

10 MS. RICHARDS: Yes, the only thing I 

11 would say is we've been asking some different 

12 questions to try and tease out some of this 

13 conversation as we go through different programs. 

14 And the questions we've been circling 

15 around, which are a little bit different than, 

16 you know, is this lawful. It's more, what is the 

17 type of the data? How intrusive is the data? 

18 How broad is the collection? In other words, am 

19 I obtaining a lot of people who are sort of an 

20 incidental collection, are not part of the target 

21 or not? And then what are the stated uses or 

22 future uses? 
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1 And we've sort of been using those three 

2 questions to get at, I think, the overall risk, 

3 which this sort of bubbling it really up is we 

4 want to stop the government from doing bad things 

5 to good people. 

6 And so you know, sort of looking through 

7 those different lenses it helps us do that 

8 analysis.

9 MS. COLLINS COOK: So thank you. David 

10 -- I'm sorry.

11 MS. BROWN LEE: I was just going to say, 

12 just because you wanted-- all right.

13 It's an iterative. I was just going to 

14 just sort of follow-up on the comments in that I 

15 think that forcing mechanism of trying to do, of 

16 having ongoing vetting and ongoing evaluation by 

17 the right people is where to go, because in 

18 looking for the meaningful relationships and 

19 developing those, as opposed to, you know, 

20 retaining the isolated pieces. 

21 So I would just say that trying to force 

22 that mechanism of ongoing vetting is really 
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1 important.

2 MR. MEDINE: One of the reasons for 

3 having the forum today is to get a better 

4 understanding of what privacy interests are being 

5 protected by your offices and our agency. 

6 And Alex and Erika have both been in 

7 either the private sector, in the case of 

8 Marriott or at least at FTC had a private sector 

9 focus. How would you compare the privacy 

10 interests you were trying to protect in your 

11 prior positions with the interests that you are 

12 trying to protect now? What are the similarities 

13 and what are the differences?

14 MR. JOEL: So I actually think there are 

15 a lot of similarities, but there are of course 

16 some important differences as well.

17 So on the similarity side, and I think 

18 privacy officers and people in all kinds of 

19 organizations, be they private sector or other 

20 government agencies, share a similar challenge or 

21 problem set, which is when your organization 

22 wants to do something either for a business 
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1 purpose or for an authorized statutary purpose, 

2 and in order to do that you need information. 

3 And for businesses this is typically 

4 information about customers or potential 

5 customers. And then you want to do something 

6 with that information to carry out your lawful 

7 activity. So it's a given that your organization 

8 will be obtaining and using personal information 

9 in many cases.

10 And so then the privacy officers' 

11 challenge is then making sure that that activity 

12 is conducted in a way that maintains your key 

13 trust relationships. 

14 There are different ways of framing it, 

15 but I think that's generally speaking what 

16 happens. 

17 And so for a business perspective, what 

18 you want to make sure you're doing is delivering 

19 value to your customer and that you're not using 

20 that information for inappropriate means or ways 

21 that are going to essentailly get your customer 

22 upset and have your customer take his business 
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1 elsewhere.

2 And so a lot of those things are 

3 similar. I think that the key distinction for a 

4 business is of course that it has the ability to 

5 disclose a lot about what it's doing in terms of 

6 obtaining that information. And the value that 

7 it's providing is also something that gets 

8 immediately, it should be immediately apparent to 

9 the customer. 

10 To the extent that the value is further 

11 down the chain and the customer doesn't see it 

12 that much, but is aware that the information is 

13 being collected, that impacts the trust that the 

14 customer has with the business.

15 I think from an intelligence community 

16 perspective, it's hard for us to demonstrate the 

17 value. What are we doing with the information? 

18 And so as a result when people are worried about 

19 information being obtained by the intelligence 

20 community, the value to them seems inchoate, yet 

21 the risks seem very real. Like, well, my freedom 

22 could be impacted if the government misuses this 
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1 information. 

2 We can reassure people we have methods 

3 in place to make sure that the information will 

4 not be misused, but I think, and we need to do a 

5 better job of that, but I think the other side of 

6 that equation is we have to show, better show 

7 what we're doing with the information.

8 And of course for intelligence agencies 

9 some of the most tightly held secrets are the 

10 successful use of intelligence, because we don't 

11 want our adversaries to know that that method was 

12 successful.

13 MS. BROWN LEE: Okay. So just to 

14 quickly answer your question, so I was also in 

15 the private sector at a law firm and practicing 

16 privacy. 

17 Here's where they're similar, whether 

18 it's clients, or even from a government 

19 prospective, people tend to be reactive to 

20 privacy. 

21 And one of the things that I find the 

22 biggest challenge is to be proactive. And it 
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1 means sometimes taking unpopular positions, 

2 whether it's with clients or internally within my 

3 organization. But sort of having principled 

4 reasons for doing that, and if not forcing 

5 putting, you know, very strong arguments to do 

6 what you think is the right thing, I think is 

7 where it's simialar and where it's hard but 

8 interesting.

9 MR. MEDINE: Becky, you talked about 

10 catagorizing certain types of information as 

11 being sensitive. 

12 In our morning discussion there was a 

13 lot of talk about the mosiac theory where there 

14 may be individual bits of information that are 

15 innocous on their face but in combination they 

16 present a perhaps sensitive profile of someone's 

17 activities, thoughts and so forth.

18 Do you lose something if you focus on 

19 what seems to be sensitive information and not 

20 take into account the potential combinations of 

21 information?

22 MS. RICHARDS: So actually the goal is 
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1 to take in to all those combinations. So the 

2 idea and where we've been looking at is that it's 

3 very difficult. You know, we want to push folks 

4 and I will say that this is an uncomfortable 

5 place to be as a privacy person. This is sort of 

6 where I'm like, well, it'll depend. 

7 But if we look at where big data is 

8 today, there is a lot of data and it's very 

9 volumonous and it's a lot of discrepancies. And 

10 if we can start to define, which is sort of what 

11 I felt like we heard in the second panel, even 

12 if -- and this is where sort of I think we're 

13 going to try and push NSA, is if we can start to 

14 define and put some mathematics behind it. 

15 So that, for example, if you have 

16 vaguely anonymous or slightly de-anomoized data 

17 over here and over here and the computers start 

18 to put them together, we would want the system to 

19 then pop something to say, hey, look at this 

20 before you decide to go forward.

21 So the idea is technology is supporting 

22 the privacy analysis by looking at whether or not 
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1 the math underneath it can work.

2 And so you're going to have to make some 

3 really hard choices. Do I think health data is 

4 more risky to privacy than my address? And 

5 everybody gives the example of, well, then you 

6 have the violence against women or, you know, 

7 something along those lines. 

8 But at some level if we deal with only 

9 those edge cases, we're not going to move 

10 forward. And I think the value, we will be 

11 losing some of the value, both from a privacy 

12 perspective, as well as from a technical 

13 perspective. 

14 Because we're sort of in this art form 

15 of looking at each individual case, which I 

16 recognize at NSA, I'm not going to be able to 

17 look at every single, little thing. We want a 

18 system to be able to identify the things that 

19 need additional analysis, that need that 

20 additional judgment.

21 But what I don't want to have happen is 

22 have us backed into a place where the system is 
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1 doing things that we would find unacceptable 

2 because we didn't sort of build something in to 

3 help with that.

4 MS. COLLINS COOK: Thank you. Rachel.

5 MS. BRAND: Thank you all for being 

6 here. For those of you who have been here all 

7 day you'll know that this is a little bit of a 

8 hobby horse of mine. But I want to ask about the 

9 FIPS and why you are purporting to apply them, 

10 although you can't really apply them.

11 So I gather, and Ms. Richards, this is 

12 directed to you, at least initially. And I 

13 commend you for publishing the paper on targeted 

14 collection under 12333. And you said that you 

15 were applying the FIPS, and I gather you were 

16 talking about the 2008 DHS iteration of the FIPS. 

17 But then you said that, for example, the 

18 individual participation FIPPs can't really apply 

19 to your activities, and the transparency one can 

20 apply in a very limited way.

21 I guess I'm wondering whether it doesn't 

22 make sense to come up with a new set of 
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1 principles that applies to survellance activities 

2 of the government? Because if you look at the 

3 DHS FIPS, the transparency one as articulated in 

4 this document really can't apply to you because 

5 it's talking about providing notice to the 

6 individual regarding collection. That's 

7 obviously not going to take place. Individual 

8 participation really can't apply at all.

9 MS. RICHARDS: Correct.

10 MS. BRAND: Some of these other ones are 

11 very, very important. Purpose specification is 

12 very important. Miniminization, data security, 

13 some of these are important. 

14 But yet, this doesn't at all address 

15 things like thresholds, evidentary thresholds for 

16 collection, which are required obviously by law. 

17 But if you're talking about principles that are 

18 supposed to sit on top of the fundamental legal 

19 requirements, you should talk about thresholds. 

20 And there are some other principles that don't 

21 come into play here. 

22 So I'd be interested in knowing why you 
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1 decided to apply the FIPS and if you've given 

2 some thought to coming up with some new 

3 principles.

4 I don't mean to critize this for DHS's 

5 purposes because DHS has a lot of functions that 

6 involve voluntary interaction by an individual 

7 with the government where this makes a lot of 

8 sense. So but you're in a different positoin 

9 than that, obviously. 

10 MS. RICHARDS: So I guess what I would 

11 say is it's a beginning place, and I've sort of 

12 stated that a couple of different times because I 

13 wanted to start with something.

14 And so from my perspective, I guess I 

15 want to take the parts of the FIPS that work 

16 well, which would be basically the bottom six of 

17 the DHS ones and then look at how we can work 

18 those through.

19 So what I would say is sometimes there's 

20 analysis that needs to be done at an enterprize 

21 level. So it's useful for me walking into the 

22 agency, which may be readily apparent to 
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1 everybody, but it was just useful to go through 

2 the process and say, hey, here is sort of one 

3 framework that we think about for privacy, and as 

4 an enterprise we don't do the first two. 

5 One of the questions that led me to ask 

6 in some of the conversations I've had with 

7 academics and advocates is to say, okay, we don't 

8 do transparency in the traditional sense and we 

9 don't do individual participation. Is there some 

10 proxy? Is there some additional thing that we 

11 should be doing, given that?

12 And I think that gets to your question 

13 of, well, are there other things that should be 

14 underpinning these? And that's where we're 

15 starting to work through those questions.

16 So I think it was very beneficial to 

17 start with that as the beginning one and then use 

18 the remaining six principles as the basis for 

19 some of these questions. 

20 Part of the problem though I will tell 

21 you with the FIPS is they don't give you a 

22 judgment. They don't tell you, well, this is 
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1 good enough or that's bad enough, which sort of 

2 gets to your evidentiary purpose. And that's the 

3 place where we are trying to then look at the 

4 data. What are the risks to the data? 

5 We spend a lot of time now talking 

6 about, well, what is the exact risk to this 

7 program to privacy and civil liberties? And so 

8 we're still working through those and having a 

9 lot of really fun and intellectually stimulating 

10 conversations about what are the right questions 

11 and how do we do that for an intelligence agency 

12 at NSA. 

13 But I would just say that it, for us, 

14 was a beginning place. I don't think that's it's 

15 necessarily the ending point, but it was 

16 someplace to start with. And I don't want to 

17 sort of throw everything out and start with, I 

18 don't know. You know, you have to start 

19 somewhere.

20 MS. BRAND: Okay. Do the other 

21 panelists want to say anything about that? Alex?

22 MR. JOEL: I would just say that even 
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1 though the first two do not directly apply, 

2 certainly not as written by DHS, they provide 

3 useful measures for us to determine to what 

4 extent does this raise privacy issues and in what 

5 areas. 

6 So that is, I think it's very helpful to 

7 use as a guide in the way that Becky has been 

8 using it at NSA. 

9 I like the idea of developing a 

10 statement of principles that would apply to the 

11 intelligence community. So I'll take that path.

12 MS. BRAND: I think I probably don't 

13 have time for another question, but I would 

14 suggest if you're going to engage in that 

15 exercise that you look at the threshold question 

16 and that you also look at oversight because 

17 these, you know, they talk about accountability 

18 and auditing, but creating a paper trail is not 

19 the same thing as effective oversight. And 

20 obviously, as I said in the previous panel, 

21 oversight is extremely important in this context. 

22 So just food for thought.
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1 MS. RICHARDS: And I think it's just 

2 important that you don't have a check box. I 

3 mean part of the problem I think with the FIPS 

4 also is it leaves itself to a little bit of a 

5 check box process. 

6 Do I have a privacy statement? Yep, I 

7 got a privacy statement. Okay, am I doing 

8 everything in there? Yep, okay, I can do that. 

9 As opposed to these sort of questions 

10 of, should I be doing that? And that's I think 

11 where having an individual at the agency whose 

12 focal point is this, really benefits the agency 

13 in terms of that conversation because it can very 

14 quickly devolve to, I checked it off, I'm good. 

15 You have no privacy, but I'm good. 

16 MS. BROWN LEE: And I would just say 

17 that the oversight perspective has to also be 

18 iterative and changing because I think as 

19 technology allows us to, you know, collect more 

20 data and in different ways and different data 

21 points that the oversight of it has less meaning 

22 if you're not also adapting on that side as fast 
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1 as we are adapting to the technological changes.

2 MS. COLLINS COOK: Thank you.

3 Jim, do you have some questions? 

4 MR. DEMPSEY: Thank you. Thank you to 

5 the members of the panel. 

6 I have some questions that I want to 

7 ask, but I saw there are a lot of audience 

8 questions. Was there one or two that stood out 

9 particularily? Technically, we only have five 

10 more minutes to go on this panel, so I'm happy, 

11 Beth, to have you ask one or two of the audience 

12 questions.

13 MS. COLLINS COOK: Sure, and I think you 

14 should know you have won the jackpot thus far on 

15 audience questions. 

16 Alex, this one goes to you and it draws 

17 on a remark from a previous panel. 

18 Why can't the IC inform the American 

19 people about how many phone records were 

20 collected pursuant to Section 215 and make 

21 similar public disclosures regarding the breadth 

22 of U.S. person collection under 702 and E0 12333? 
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1 So the executive order, understanding 

2 that you're not targeting necessarily U.S. 

3 persons, but the U.S. person incidental 

4 collection. 

5 MR. JOEL: So that's a good question. I 

6 don't want to duck it. I'll just say that I am 

7 going to in certain ways. 

8 No, but I don't want to, I guess I'm not 

9 going to get into the specifics of like 215 or 

10 702, etcetera. 

11 What I'll say is that there are two 

12 challenges. I understand the interest and I 

13 understand the importance. One is technical 

14 capability. Can you, in fact, count it? And for 

15 some things, some activities, you should be able 

16 to count. But for some other ones, they 

17 inherently involve challenges.

18 I know that one of the PCLOB 

19 recommendations in the 702 report was, in fact, 

20 to count some of the 702 collection that involves 

21 U.S. persons. So there are some inherent 

22 challenges in doing that. 
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1 From a national security perspective 

2 what I'll say is what I have heard internally as 

3 we have pursued these kinds of questions is that 

4 providing that kind of information can, in fact, 

5 put at risk some kinds of collection, especially 

6 if you track it over time.

7 An adversary, a sophicated adversary can 

8 put the information together in terms of the 

9 volume of collection in one particular area and 

10 then draw some conclusions about what 

11 specifically is being obtained, what are the 

12 specific channels that are being watched, and 

13 therefore change behavior. 

14 So our job from a transparency 

15 perspective is to continue to discuss that 

16 internally and see, well, you know, are there 

17 ways of mitigating that? What can we, in fact, 

18 disclose in this area? Because it's of strong 

19 interest.

20 MS. COLLINS COOK: So, Erika, I'll 

21 direct this next one to you because you mentioned 

22 that part of the civil liberties protections and 
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1 privacy protections are consequences for 

2 wrongdoing. 

3 So the question from the audience is, in 

4 the case of a privacy violation sufficient 

5 remedial measures are critical. What, if 

6 anything, do you think needs to be done, either 

7 statutorily or administratively to strengthen 

8 existing remedial schemes?

9 MS. BROWN LEE: So, yeah, I do think 

10 that the remedies for Privacy Act violations or 

11 for privacy violations are, you know, as I said 

12 in my remarks everything could be examined and 

13 looked at for approval.

14 I was focusing my remarks on FBI. So of 

15 course they have their own investigative unit 

16 that reviews. So if there's any particular 

17 activity that an agent engages in, for example, 

18 that is, you know, collecting information in 

19 violation or specifically because of First 

20 Admendment purposes, that's subject to review and 

21 discplinary action.

22 With regard to individuals, I agree. I 
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1 mean we talked about how the FIPS doesn't really 

2 have as meaningful of really a guide for law 

3 enforcement either.

4 I think, you know, it's not something 

5 that I can do, but certainly it's been attempted 

6 before to remedy the Privacy Act or to amend it. 

7 We are, the administration is committed 

8 to looking to expand the protections of judicial 

9 redress for non-U.S. persons, and DHS has a 

10 policy of doing so administratively. 

11 But I think statutorily it's a hurdle. 

12 I think it's something that I would be willing to 

13 have a conversation to further that.

14 MS. COLLINS COOK: So just to keep this 

15 even across the board, Becky, this one is for 

16 you.

17 And I think implicit in this question is 

18 a very interesting premise. Do you anticipate 

19 that wide swaths of data will no longer be 

20 collected now that you are asking questions about 

21 whether they are really needed and the civil 

22 liberties downsides?
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1 So I would say the premise is that it's 

2 your job to shut it down, which I think it's a 

3 widely shared premise. 

4 And I think the basic question is, do 

5 you think you're going to be effective?

6 MS. RICHARDS: So I think that also 

7 starts with the premise that the collection we're 

8 doing currently -- that's starting with the 

9 premise that we're collecting too much 

10 information today.

11 And I think what I would say is that 

12 what we're working on is sort of a premise, so if 

13 NSA is filled with a lot of people who do math 

14 for a living, we're in the process of third grade 

15 math, which is folks need to show their work. So 

16 they need to show why they're doing what they're 

17 doing so that then we can have those 

18 conversations.

19 I don't want to presuppose we're going 

20 to do more, or less, or either way of those. But 

21 I do think that what we haven't done well is 

22 explain what we're doing. 
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1 And if you sort of consider that NSA has 

2 a long history of saying absolutely nothing to 

3 anyone, and in the last year and a half we've had 

4 to create a voice for ourselves to explain what 

5 it is we do, and recognize that most people, 

6 there are a lot of Ph.D.s in math at NSA who 

7 don't necessarily take well to speaking in 

8 public. It's a work in progress. 

9 And so my hope here is not to be judged 

10 by how much we turn on or turn off, but by 

11 demonstrating what the value is to the country in 

12 terms of what we're doing and demonstrating that 

13 we're protecting civil liberties and privacy.

14 MS. COLLINS COOK: So thank you all for 

15 your remarks and your active back and forth on 

16 the questions.

17 MR. MEDINE: And we'll be taking a 10 or 

18 15 minute break, and at 2:45 we'll resume with 

19 the private sector's views on these issues. 

20 Thanks.

21 (Off the record.)

22 MR. MEDINE: Good afternoon. Thanks for 
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1 everyone's endurance who's been here all or most 

2 of the day. 

3 This is our final panel today, but an 

4 important panel on what the private sector has 

5 learned about privacy and how that might relate 

6 to the considerations we go into with regard to 

7 national security issues, and Rachel Brand will 

8 moderating.

9 MS. BRAND: All right, thank you, David, 

10 and thank you to all our panelists for being 

11 here. 

12 The way we've structured the day is that 

13 the first panel this morning had to do with the 

14 theoretical underpinnings of privacy and 

15 exploring what interests underlie privacy. The 

16 second panel had to do with technology. The 

17 third panel was a government panel. 

18 And this last panel is supposed to be 

19 focused on solutions, and particularly those 

20 solutions that folks in the private sector might 

21 be able to suggest. 

22 So what we'll do here logistically is 
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1 each panelist will start with up to seven minutes 

2 of remarks. And for the panelists' benefit, Sam 

3 Kaplan is sitting in the front row here with 

4 yellow and red cards. So when he holds up the 

5 yellow card, you'll know you have two minutes 

6 left, so please pay attention, and the red card 

7 means that your time is up. 

8 At that point, as the moderator I will 

9 ask about 20 minutes of questions, and then each 

10 of my fellow Board members will have five minutes 

11 of questions, and then we'll open it up to 

12 questions from the audience. 

13 And as with the previous panels, when I 

14 start to ask questions some of our staff members 

15 will stand up in the back, and Lynn Parker Dupree 

16 in particular, and Prem, will stand up and hold 

17 up cards and you can go get yourself a card, 

18 write down your question and then the staff will 

19 pass it up here. 

20 So we'll just go down the row and we 

21 will start with Professor Cate. I am not going 

22 to go into length on their biographies because I 
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1 think they're all available to you.

2 But Professor Cate is a Professor at the 

3 University of Indiana School of Law, and he's 

4 been on a number of previous boards and 

5 commissions on privacy.

6 And so, Professor Cate, let's start with 

7 you. 

8 MR. CATE: Thank you very much. This is 

9 the time I think to say that I'm colorblind so 

10 I'll have no idea what cards you're holding up. 

11 So perhaps you'll wave them in a definitive way 

12 and I will pay attention. 

13 So first of all, I was sorry not to be 

14 here for this morning, but the last panel was 

15 absolutely superb, and it's a privilege, both to 

16 be here, and I really want to applaud the Board 

17 for taking up this I think really difficult, but 

18 fundamental, issue about what is privacy and how 

19 in practice might we go about protecting it, both 

20 in the private and the public sectors.

21 I want to really just offer some 

22 observations, as opposed to any specific, if you 
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1 will, recommendations or conclusions. 

2 One, and this was touched on in the last 

3 panel, I think the FIPPs are frankly not 

4 tremendously useful. 

5 I'm not suggesting abandoning them, 

6 which is a big change for me. Ten years ago I 

7 wrote a book chapter called, the death of the 

8 FIPPs. But fortunately, I've gained a little bit 

9 of knowledge here. 

10 But I think we use them almost 

11 talismanically, like we can roll out these eight 

12 principles, or depending on what list of FIPPs 

13 you use, and that that will get us somewhere. 

14 And that far too frequently, both in the private, 

15 and certainly in the public sector, they really 

16 don't get us anywhere.

17 What we end up is we end up, just like 

18 talked about in the last panel, looking for 

19 substitutes for the FIPPs. Well, we can't have 

20 consent, what could we have, rather than asking 

21 what was the purpose to be served in the first 

22 place? 
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1 And maybe consent's no longer relevant 

2 as a tool to achieve that purpose, rather what 

3 are we trying to actually do here? Really the 

4 question you've been asking all day, what are we 

5 trying to protect? What do we think protecting 

6 privacy really means? 

7 I say this, by the way, about the FIPPs 

8 in part because I'm not sure that they've ever 

9 worked terribly well, and certainly in the U.S. 

10 environment where they've largely come to mean 

11 notice and choice. 

12 I'm not sure that they work well in a 

13 world of massive data, whether we call it big 

14 data, or whether we call it just high volume 

15 data, but the notion of a sort of FIPPs-like 

16 approach, particularly with a focus on the 

17 individual when the broader issues may be, 

18 frankly, societal. They may be the impact on the 

19 economy. They may be the impact on civil 

20 liberties, not of one person but of everybody. 

21 And I don't know that the FIPPs help focus us in 

22 a useful way on that. 
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1 And then frankly, I think the FIPPs have 

2 led to some sort of silly results. And you know, 

3 I would just mention I've always been surprised, 

4 for example, the Department of Homeland Security 

5 privacy impact assessment on border searches of 

6 electronic devices, which focused a lot on notice 

7 as a privacy protection. 

8 Well, at the point that your device has 

9 been seized from you and its contents copied it's 

10 difficult to think that notice is meaningful 

11 protection. It may be necessary, but whether 

12 it's protection or not, I think it's not. 

13 Second point, one of the things we are 

14 seeing emerging in the debate in the private 

15 sector, and we see this especially in Europe in 

16 the context of discussing the general data 

17 protection regulation there, is greater focus on 

18 risk management, or risk assessment and risk 

19 management. 

20 And I don't mean to use this just 

21 because it's sort of the jargon of the day but 

22 rather because risk management is an incredibly 
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1 valuable tool that in privacy we are unbelievably 

2 far behind on. You know, security we have much 

3 clearer ideas what risk management mean. Privacy 

4 we really lack that understanding.

5 And part of the reason is because we 

6 don't know what risks we're guarding against. 

7 We're very unclear what are the harms, what are 

8 the impacts, what are the negative effects that 

9 we think we are balancing, if you will, with the 

10 positive outcomes of the use of data or what have 

11 you.

12 And so one reason I think the risk 

13 management approach offers a lot of value in both 

14 public and private sector is that it makes us 

15 stop and say, what is it we're trying to 

16 accomplish? What are the positive benefits and 

17 what are the potential negative impacts, not 

18 measured in terms of FIPPs, but measured in terms 

19 of actual impact on individuals, or on society, 

20 or on the economy, as we think about it. 

21 When using risk management, or if you 

22 hate risk management, in either case, third 
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1 point, I think there's a lot of reason to focus 

2 more attention on use of data. 

3 And this has been a real weakness of the 

4 U.S. legal system. Those of you who have 

5 suffered through law school know that the Fourth 

6 Amendment has almost nothing to say about use of 

7 data whatsoever. 

8 In fact, you can have illegally seized 

9 data that the court acknowledges is illegally 

10 seized and they will still allow it to be used 

11 someplace else because there would be no 

12 disincentive for the collection, it's only the 

13 collection in the Fourth Amendment that Supreme 

14 Court jurisprudence has been focused on.

15 And for this reason I think we really 

16 would be better to be thinking more about 

17 reasonable and effective limits on use. And in 

18 fact, I think that's what the public most 

19 commonly cares about.

20 And one of the practical reasons for 

21 that is because there's almost always a 

22 legitimate reason to collect the data. There's 
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1 always some reason, there's some employment 

2 reason, there's some security reason, there's 

3 some private sector reason. You know, Verizon 

4 had a reason to collect this data. And then the 

5 question was, who could access it and how could 

6 it be used? 

7 But our legal system has focused 

8 enormous attention on collection, and then once 

9 the data are in the government's storehouse then 

10 we feel that the data are more commonly out of 

11 control. And I think that is a critical area to 

12 focus on as well. 

13 Fourth, as I've mentioned, I think the 

14 Fourth Amendment, while it's a critical legal 

15 limit and I certainly concur -- that's yellow, 

16 right? Yes? Thank you. And for the rest of 

17 you, you'll know I just got a yellow card.

18 I think the Fourth Amendment of course 

19 is a critical legal limit and we must of course 

20 observe it. It's not a very useful guide for 

21 telling you what to do in the future for a 

22 positive analysis of privacy issues. 
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1 And I think we should again be careful 

2 about that. Too often in our rhetoric we say 

3 well, it's permitted under the Fourth Amendment 

4 as if that tells us anything, other than it is 

5 not illegal under the Fourth Amendment. But it 

6 doesn't tell us anything about either the ethics, 

7 or the desirability or what have you of doing it. 

8 And then fifth, I would just say in 

9 almost all of these areas, and I understand in 

10 national security this seems particularly maybe 

11 odd, I think redress is something we need to 

12 continue to focus on. 

13 We see many uses of data in the 

14 government setting and in private sector which 

15 are done without regard to redress, with just 

16 sort of, well, if it affects a person 

17 inaccurately every now and then, what does it 

18 really matter? We'll deny boarding to people on 

19 airplanes, or we'll provide extra security for 

20 the wrong people. 

21 This is not an efficient use of 

22 government resources and it's not a good way to 
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1 think about privacy. 

2 And I think we should be very clear in 

3 those rare exceptions where we say there might be 

4 no redress available here for the individual, in 

5 which case we now have to provide it through 

6 other means, inspector generals, or the PCLOB, or 

7 other ways of approaching it. 

8 But at all times we should be thinking 

9 about redress, not just because of the rights of 

10 the individual, but because of the interests in 

11 ensuring that the system works as advertised and 

12 as it should. Thank you very much.

13 MS. BRAND: Thank you very much. Our 

14 next panelist is Harley Geiger. Harley Geiger is 

15 Advocacy Director and Senior Counsel at the 

16 Center for Democracy and Technology, and he 

17 focuses on issues related to civil liberties and 

18 government surveillance, computer crime and 

19 cybersecurity. Thank you for being here. 

20 MR. GEIGER: Members of the Privacy and 

21 Civil Liberties Oversight Board, thank very much 

22 for inviting me to speak at your meeting today, 
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1 and thank you also for your excellent work on 

2 ensuring protection for privacy and civil 

3 liberties in national security and terrorism 

4 programs. And congratulations on having one of 

5 the best acronyms in town.

6 When it comes to evaluating privacy 

7 protection, the Center for Democracy and 

8 Technology believes that the Fair Information 

9 Practice Principles are a very important 

10 framework for both government and the private 

11 sector. 

12 Now you can add other privacy frameworks 

13 on top of that. We certainly do not disagree 

14 with Professor Cate that societal impact is a 

15 very useful consideration, and we certainly agree 

16 that protections focused on the purpose of data 

17 collection are also useful. 

18 But we view the FIPPs as an 

19 indispensable framework for evaluating privacy 

20 protection with data collection practices.

21 Now the individual principles of the 

22 FIPPs, as you know, are overlapping and mutually 
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1 dependent on one another. It is a framework. 

2 It's not a smorgasbord that you can just choose 

3 and pick, at least not unless you don't want 

4 robust privacy protection. 

5 And there is obviously some discussion 

6 in the private sector about doing away with data 

7 collection limitations or the data minimization 

8 principle of the FIPPs, seeing as how we are now 

9 all in an age of big data. 

10 But in the time that you've given me, I 

11 want to address this head-on in the context of 

12 government surveillance.

13 First, CDT believes that there still 

14 should be collection limitations on private 

15 sector data collection, and that the data 

16 minimization principle of the FIPPs should apply 

17 to the private sector. 

18 Second, the government should not take 

19 its cues entirely from the private sector when it 

20 comes to national security surveillance. Data 

21 collection from the private sector is 

22 fundamentally different from national security 
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1 surveillance.

2 Therefore, even if the private sector 

3 were to collect data in a relatively unrestrained 

4 manner in some alternate universe, then 

5 intelligence agencies should still nonetheless 

6 not follow suit. 

7 The missions of the private sector and 

8 the national security functions of governments 

9 are totally different. That should go without 

10 saying. The private sector typically does not 

11 use the data that it collects to detain or take 

12 kinetic action against the individuals as part of 

13 its mission. 

14 Several major private sector companies 

15 have repeatedly responded to public outcry over 

16 privacy with enhanced transparency and privacy 

17 controls. 

18 The national security arms of government 

19 are not as transparent or responsive and are not 

20 likely to be. 

21 Many major companies, in addition, allow 

22 or are required by law to allow consumers to 
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1 limit the collection of information about them. 

2 More and more services are differentiating 

3 themselves on the basis of strong privacy 

4 protection. 

5 And of course, individuals can choose 

6 not to participate in a commercial service as a 

7 means of limiting direct data collection about 

8 them. But data collection for national security 

9 purposes does not permit any meaningful choice. 

10 So this is not to laud private sector 

11 data collection practices because CDT does view 

12 them as generally insufficiently protective of 

13 privacy. 

14 But because of the differences that I 

15 just briefly listed, and other reasons, even if 

16 the private sector fails to robustly apply the 

17 FIPPs government agencies should not follow suit. 

18 If anything, because of these 

19 differences government should strive for a more 

20 strict and consistent application of the FIPPs 

21 than that of private sector data collection. 

22 And so I have a small set of broad 
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1 recommendations to make. 

2 First, the government should place 

3 greater emphasis on applying the data 

4 minimization principle of the FIPPs. Back-end 

5 minimization procedures alone are not sufficient. 

6 Front-end minimization is also critical. 

7 Trust is breached at the point of 

8 collection. Once the government collects 

9 information, non-statutory internal restraints on 

10 access and use can fall away like sand castles on 

11 a beach. We saw this happen with the 702 

12 backdoor search loophole. 

13 So surveillance should be restricted at 

14 the front-end by narrowing limiting the 

15 collection of data to what is directly needed to 

16 accomplish a specific purpose. 

17 The data should then be retained only as 

18 long as is necessary to fulfill that purpose, and 

19 the data should be destroyed unless a 

20 determination is made that the data are needed to 

21 accomplish the specific purpose.

22 The specified purpose of data collection 
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1 itself should be subject to meaningful 

2 restriction. 

3 For example, limiting the scope of what 

4 is relevant under Section 215, or the definition 

5 of foreign intelligence in Executive Order 12333. 

6 So the goal should be overall to move 

7 from mass data collection to targeted data 

8 collection of both U.S. and non-U.S. persons. 

9 Second, the government should provide 

10 much greater transparency regarding the 

11 interpretation of surveillance laws. Section 215 

12 of the PATRIOT Act exemplifies this. 

13 Nobody was surprised that the NSA is 

14 collecting phone records. What was surprising 

15 was that the NSA has secretly interpreted Section 

16 215 to allow for the collection of all phone 

17 records in the entire country. 

18 This is bad data minimization. And yet, 

19 a fair reading of the statute does not seem to 

20 grant them with this authority. 

21 So declassification of FISA court 

22 orders, or, when necessary, summaries of opinions 
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1 would substantially boost transparency. We 

2 should not be a nation of secret laws. 

3 Third, the government should provide 

4 greater transparency around the extent and scope 

5 of requests for data under national security 

6 authorities. This includes government reporting 

7 about its national security surveillance 

8 activities, such as how many requests were made, 

9 under which surveillance authorities, and for 

10 what type of data, as well as how many U.S. and 

11 non-U.S. persons were affected. 

12 The government should authorize the 

13 private sector to make similar reports. 

14 Information is power and privacy is 

15 control of information. An entity possessing 

16 information about an individual has power over 

17 that individual. 

18 Large scale government collection of 

19 information about individuals threatens the 

20 relationship between citizens and the state 

21 because it upsets the balance of power that 

22 supposedly exists in democratic society.
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1 Therefore, CDT urges PCLOB to recommend 

2 that the government recommit to a robust 

3 application of the Fair Information Practice 

4 Principles, as well as other considerations, 

5 regardless of what the private sector does, with 

6 much more targeted data collection and greater 

7 transparency. Thank you.

8 MS. BRAND: Thank you. Our next 

9 panelist is John Grant. Mr. Grant is a Civil 

10 Liberties Engineer at Palantir Technologies, and 

11 he previously served on the staff of the Senate 

12 Homeland Security Committee where, among other 

13 things, he oversaw the Department of Homeland 

14 Security. Thanks for being here.

15 MR. GRANT: Thank you very much, and 

16 thank you for the invitation to speak today.

17 As I never tire of telling people, I was 

18 a congressional staffer who worked on the 

19 legislation creating PCLOB 2.0, so I take a pride 

20 of parentage in the Board and I'm sure it's every 

21 parent's dream to one day testify in front of 

22 their children.
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1 I know that the Board and a lot of 

2 people are familiar with Palantir so I'll spare 

3 everybody the extended commercial. Just suffice 

4 it to say, Palantir builds a data management and 

5 data analytics platform that works with data.

6 We started in the law enforcement, 

7 intelligence space and have expanded to 

8 deployments around the world and in a variety of 

9 contexts in the financial sector, medicine and 

10 elsewhere.

11 A core tenet of Palantir is that our 

12 technology isn't successful if in the course of 

13 achieving an organization's analytic mission 

14 we're not also able to be deployed in a way that 

15 protects privacy. 

16 And that's something that the founders 

17 of the company instilled from day one, and that 

18 is why my job exists, a civil liberties engineer. 

19 Well, one of the things I learned when I 

20 went to Palantir, and this is different from the 

21 Hill certainly, is when you walk into a room and 

22 you say to engineers, I'm worried about this 
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1 thing you're building, it creates a privacy 

2 problem. The response is, oh, okay, how do I fix 

3 it? Which is not often what you get sometimes 

4 when you raise these things in other places. 

5 So it's our job as a civil liberties 

6 engineering team to come up with suggestions for 

7 how to fix it. 

8 I am a lawyer, as you may have guessed, 

9 so I do not necessarily possess a lot of 

10 technical skill. So the main role for us is to 

11 translate between the lawyers and the engineers 

12 and back. 

13 So what I wanted to focus on today a 

14 little bit is some of the technology at a high 

15 level, and then I had some actually suggestions 

16 for moving forward that I think are actually 

17 fairly low hanging fruit. 

18 So just briefly to provide a little 

19 context, as I said, Palantir is data management 

20 and data analytics, so we're not dealing with the 

21 collection of data. So this gets more to 

22 Professor Cate's point about the use of data. 
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1 And we have two sort of high level 

2 categories of technology that deal with managing 

3 or protecting privacy with the use of data, and 

4 that's access controls and oversight mechanisms. 

5 But I want to start by pointing out, and 

6 I think this is something to keep in mind, that 

7 just as technology has expanded the power of 

8 surveillance today and the amount of data that 

9 can be collected, it's also significantly 

10 expanded the level of privacy protection that is 

11 available at the agencies.

12 If you imagine 50 years ago if there was 

13 an FBI file, this was probably pieces of paper in 

14 a Redweld sitting on a desk somewhere, or maybe 

15 locked in a desk drawer, hopefully locked, or 

16 maybe in a dusty basement archive or something 

17 like that. 

18 And you know, there'd probably be very 

19 limited tracking of where the file was, you know, 

20 hopefully a log book with a name and who had the 

21 record. But who knows? 

22 And anyone who accessed the file would 
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1 be able to see whatever was in the Redweld. 

2 You'd just be able to rifle through it and you 

3 could see anything, even if it wasn't directly 

4 relevant to what you needed. 

5 Oversight into how the file would be 

6 used would really be nonexistent. You wouldn't 

7 see exactly who added information to the file, 

8 who deleted information from the file. And 

9 deletion would largely be hopefully a burn bag or 

10 a shredder, probably just crumpling it up and 

11 throwing it in the trash. A more precise 

12 deletion would be a black magic-marker redacting 

13 a few points of information.

14 Today. So today technology allows us to 

15 do a lot more management of data and oversight, 

16 and management at a granular level, and that's 

17 what the access control point, which is you can 

18 now build access controls to manage data very 

19 precisely on a data point by data point basis. 

20 And you can do it in a more nuanced way. 

21 You don't have to make a choice between access or 

22 not access. There are ways to sort of have 
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1 gradations of access. You can make the access 

2 controls dynamic. 

3 So there's a lot of options. And the 

4 many options you have to configure those access 

5 controls give you a near infinite variety of 

6 options in how to manage the data, who can see 

7 the data and what they can do with the data. 

8 The other point is oversight mechanisms, 

9 and this is really thinking a lot about audit 

10 logging and also using technological, electronic 

11 work flows to control exactly how data flows 

12 around an organization, and who can see data, and 

13 exactly what kinds of analysis they can do with 

14 it, even automating, or at least hardwiring in an 

15 approval chain for use of data and things like 

16 that. 

17 And these can be very detailed. So the 

18 hardwired approval process and things like that, 

19 that can be very complex. It can involve 

20 multiple actors, it can involve multiple 

21 stakeholders. 

22 And then the auditing of how data is 
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1 used itself can be incredibly granular and 

2 incredibly detailed. 

3 And I'm skipping over a lot here, but I 

4 want to get to some of the other points. But 

5 just these two capabilities are a significant 

6 improvement of what existed before and can get us 

7 a long way. 

8 And there are things that exist today. 

9 Now, I'm obligated to say that of course Palantir 

10 does these the best, but these are not 

11 technologies that are exclusive to Palantir. And 

12 they can be deployed and they can be used in a 

13 lot of different contexts. 

14 So what is the problem today, and why 

15 aren't these capabilities being used more than 

16 they could be and at the level that we think they 

17 could be?

18 A couple of things. One issue is 

19 technical awareness. Lawyers don't know 

20 technology and engineers don't know law, and you 

21 need people who know both of these things to be 

22 able to make the decisions of how to use these 
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1 technologies, how to incorporate them effectively 

2 into programs. 

3 Lack of resources. You need people who 

4 can actually manage the data. And we talked 

5 about this in the earlier panel. Alex Joel has a 

6 very small staff. Erika has a very small staff. 

7 And they're managing huge amounts of data and 

8 huge organizations. They need resources, they 

9 need infrastructure to actually be able to do 

10 this. 

11 Privacy is hard. How exactly do you 

12 look at an audit log? How do you use it 

13 effectively? How exactly do you manage access 

14 controls at this data point by data point level, 

15 especially when you're dealing with mass amounts 

16 of data? 

17 And the last one is death by anecdote. 

18 The argument, the debate, the cost benefit 

19 analysis right now tends to be the national 

20 security sector saying one time we caught this 

21 bad guy using this information, and the civil 

22 liberties community saying one time this unjust 
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1 thing happened to a person because of this 

2 program. 

3 There needs to actually be a much more 

4 -- you can't just make this argument on anecdotal 

5 grounds. You have to actually look at the data 

6 and you can find out more specifically how these 

7 programs are working, how effective they are.

8 So solutions. Obviously we suggest some 

9 of the solutions in listing the problems. 

10 Education. I think, and Palantir 

11 actually sponsors fellowships with Paul Ohm's 

12 Silicon Flatirons Project and other places to 

13 make sure lawyers can learn technology and that 

14 engineers can learn law. 

15 Engineers don't have to be lawyers, but 

16 it actually should be a requirement to have an 

17 engineering ethics program and to have courses 

18 that teach engineers privacy, because they're 

19 going to build the technology that's going to hit 

20 the streets and it's going to be months or years 

21 before the law catches up. 

22 So shouldn't engineers be able to catch, 
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1 you know, how what they're building is going to 

2 affect privacy and be able to start thinking 

3 about these things? 

4 Infrastructure. If privacy is an 

5 important value for us as a society then we need 

6 to invest in infrastructure to support it.

7 Concrete guidance. We actually need to 

8 go beyond just systems should have use 

9 limitations. We need to actually tell people how 

10 are you're going to do it? And I can get into 

11 that more if people have questions. 

12 But we need to be writing really 

13 specific guidance, rather than just the, you need 

14 to have notice and consent, you should be 

15 thinking about use limitations, things like that.

16 And last, everything in the world can be 

17 datafied these days, including how these systems 

18 are working and how effective they are. And we 

19 can do the analysis, and we can get beyond 

20 anecdotes, and we can start analyzing data and 

21 figuring out is this effective, is this not 

22 effective, is this having negative effects, is 
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1 this creating bias analysis, etcetera.

2 Thanks very much.

3 MS. BRAND: Thank you. And our last 

4 panelist is Chris Inglis. He is currently a 

5 venture partner at Paladin Capital Group and is 

6 the former Deputy Director of the NSA. Thanks 

7 for being here.

8 MR. INGLIS: Great, thank you. And I'm 

9 honor bound to say that I spend most of my time 

10 teaching at the Naval Academy in the Computer 

11 Science and the Cyber Operations Department. 

12 First, I, like the other panelists, am 

13 grateful that you've established this venue for 

14 what I think is a really important dialogue, and 

15 I'd like to make four quick comments and then 

16 help us get to question and answers. 

17 First and foremost, I absolutely agree 

18 with the panel's premise, which I believe is, is 

19 that the framers of the Constitution did not 

20 intend for security and privacy to be in mortal 

21 combat and we're therefore trying to figure out 

22 how do we achieve both. 
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1 And it may well be that we cannot trade 

2 one for the other. I think that's right, we 

3 cannot, but we have to work harder to achieve 

4 both. And I think technology and practice from 

5 the private sector can be helpful there.

6 Two, I agree that government is 

7 different, not simply in the powers, the tools 

8 that it might bring to bear on its citizenry or 

9 others, and therefore should be constrained, but 

10 the government alone has the requirement to 

11 essentially meet the standards of the First, 

12 Fourth and Tenth Amendments within the 

13 Constitution. 

14 I will tell you that from my NSA 

15 experience, the Tenth Amendment was the most 

16 significant of those, which essentially says 

17 unless you have the authority to do something, 

18 you do not, you know. 

19 And against what has been said, which is 

20 that backdoor searches or 215 was NSA's 

21 interpretation, both of those were specifically 

22 permitted under court approved procedures and 
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1 specifically were interpretations of the law that 

2 went through three branches of government. 

3 I think that's right and proper. That 

4 doesn't necessarily justify them. It may be bad 

5 policy at the end of the day, but the rule of law 

6 has to pertain, right, in terms of how the 

7 government gets things done.

8 Point three, I would say that largely I 

9 agree with what John had to say. Matter of fact, 

10 I wholly agree with what John had to say, that 

11 the aspects of law and technology are often at 

12 odds with one another, not because they cannot be 

13 reconciled, but because they're perceived as 

14 independent biases on any particular solution.

15 And I would add a third, which is that 

16 what typically plays out in any one of these 

17 systems is that you're trying to effect a 

18 technology, law, and the operational practice of 

19 those who essentially make use of the technology. 

20 And the unsurprising result is that 

21 because they do not change at the same rate, they 

22 essentially change at very different rates, 
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1 keeping them reconciled or synchronized from 

2 moment to moment is really hard. 

3 Therefore, mechanisms, FIPPs-like 

4 mechanisms or other things are not likely to 

5 satisfy the need. 

6 What you need are threads or systemic 

7 solutions that essentially you pull through and 

8 you take both art and science process to 

9 essentially try to figure out how to make some 

10 solution here. 

11 I'll wholly agree with John that 

12 education's going to be absolutely essential. At 

13 NSA ultimately when we found ourselves in the 

14 midst of some compliance incidents for which no 

15 one had intentionally made a mistake, we actually 

16 had to sit down and figure out how do you achieve 

17 a horizontal join between the technologists, the 

18 legal practitioners and the operators, all of 

19 whom were trying to achieve something that was 

20 slightly different, but ultimately invested in 

21 the same problem set. 

22 The last point I would make is that I do 
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1 believe that there's a role for big data, what is 

2 sometimes called mass collection. There's a role 

3 for big data, but the principles that should 

4 pertain to the government's collection of that 

5 should be the same as surgical data, which is 

6 necessity and proportionality. 

7 The government should be able to justify 

8 on what basis this is necessary, such that it 

9 could then argue, not for an encroachment upon 

10 civil liberties or privacy, but how do we then 

11 work harder to achieve the sustainment of privacy 

12 and civil liberty. And it should only achieve 

13 that in proportion to that need. 

14 Therefore, I think that all those four 

15 comments aside, I would say that the private 

16 sector probably has a lot of experience in this 

17 regard that the government can take advantage of. 

18 My own sense is that the government 

19 collects far less information than is perceived 

20 by the public, and certainly far less information 

21 than the private sector does.

22 Again, I don't excuse the government for 
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1 that. The government should be held to account, 

2 but the government can, in fact, bring 

3 technologies in that might well scale quite well 

4 for the government's purposes because we'd have 

5 to scale them down, as opposed to scale them up.

6 I'm open to any questions you may have.

7 MS. BRAND: Thank you. Just a reminder 

8 to the audience that there are PCLOB staffers in 

9 the back with cards and if you'd like to direct a 

10 written question to the panelists, hold up your 

11 hand, find one of them and then write down your 

12 question.

13 And for the benefit of the audience and 

14 the cameras, for the panelists, when you're 

15 answering a question if you wouldn't mind moving 

16 the mic back and forth. I'm sorry, we don't have 

17 as many mics as we probably should.

18 So I'd like to start by asking about 

19 oversight, and I'd like, Mr. Grant, to direct 

20 this question to you first. 

21 Both in your oral statement and in the 

22 written statement that you submitted to us, you 
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1 talked about a wide range of mechanisms, paper 

2 trails, electronic work flows and things like 

3 that, and frankly, on the written statement it 

4 seemed like an overwhelming array of different 

5 ways to engage in oversight.

6 I think for a couple of reasons you need 

7 to choose your oversight mechanisms. One is that 

8 any agency is going to have limited resources to 

9 dedicate to oversight. 

10 And secondly, as I mentioned in a 

11 previous panel, there may come a point where 

12 there are diminishing returns on oversight. You 

13 need to leave these people doing the work at the 

14 NSA or other agencies time to actually do their 

15 job, not just comply with oversight mechanisms 

16 all day long. So you have to find some balance. 

17 So have you given some thought to what 

18 constitutes an effective oversight mechanism? 

19 How do you rank different mechanisms in terms of 

20 their effectiveness?

21 MR. GRANT: Yes, so I think we should 

22 actually think about oversight as a big data 
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1 problem and then apply the same thinking to it 

2 that we would apply to trying to analyze signals 

3 intelligence and trying to analyze huge amounts 

4 of transactional data for marketing. 

5 It's a similar issue. You have a huge 

6 amount of data, as you say. There are massive 

7 amounts of audit logs, for example, in an 

8 organization like the NSA, and that's a lot of 

9 information. 

10 But you can use technology and analytic 

11 tools to make sense of that information and 

12 derive the insights that you're looking for. 

13 So but part of the issue is, A, you need 

14 to do it, you need someone. So we see this all 

15 the time in Palantir and I know other 

16 organizations see this as well, which is, 

17 everybody checks the box on FISMA for audit logs. 

18 We've got the audit logs and we will go through 

19 an enormous number of hoops to make sure it's 

20 logging exactly the information that it's 

21 supposed to. 

22 We get fewer requests to actually look 
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1 at the audit logs once the auditing mechanisms 

2 are turned on. 

3 And looking back to my congressional 

4 experience, there aren't many laws that I can 

5 recall that tell anyone they actually have to 

6 look at the audit logs, they just have to.

7 It's the Seinfeld joke about renting a 

8 car. Everybody can take the reservation but you 

9 have to hold the reservation. You have to use 

10 the information. 

11 So I think, I mean to me that's how you 

12 make oversight more effective, you use these 

13 techniques. 

14 And that's another thing. The oversight 

15 people and the information security people and 

16 things like that, they should be as good as your 

17 analysts, and you need to have good people who 

18 are also doing the analysis and conducting the 

19 oversight. 

20 So to get to your last question, which 

21 is the most effective? I think it's using that 

22 auditing data. I think it's using that big data 
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1 that you've got and having a team of people that 

2 can proactively comb through it.

3 And not only are you going to look for 

4 people doing something wrong, but you can also 

5 ask questions such as, you know, does our data 

6 retention policy make sense? 

7 You could look at the data and say, you 

8 know what, it turns out we keep data, this data 

9 set for five years. Nobody ever uses the data 

10 older than three years in that data set, so let's 

11 change the data retention policy to fit with the 

12 actual usage of the data.

13 MS. BRAND: Thank you. Mr. Inglis, I'd 

14 especially like your thoughts from your time in 

15 government, what did you view as an effective 

16 oversight mechanism?

17 MR. INGLIS: So first and foremost, if 

18 there is an authority that is granted or a burden 

19 that's imposed, and they come hand in glove, you 

20 know, that's not a one time thing. There cannot 

21 be a repurposing somewhere later simply having 

22 gotten past that threshold. 
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1 At NSA the typical events might be 

2 constituted as collection, processing of data, 

3 analysis of data, dissemination of that data, and 

4 the burden was imposed at every step according to 

5 whatever the authorities were that were granted 

6 for the acquiring of that data, the acquisition 

7 of that data in the first place.

8 And what we ultimately found is that in 

9 order to achieve that, because data ultimately is 

10 aggregated, synthesized, a typical 

11 counterterrorism analyst, we take the iconic 

12 analytic effort, doesn't simply use data from one 

13 source, they use data from many sources. 

14 And at that point it is really hard, if 

15 there are different expectations of the different 

16 data sets to try and keep it straight in your 

17 head as to what you're going to do about that.

18 So the focus has to be how do you bind 

19 the attributes for a particular data element at 

20 the moment that it comes into being?

21 MS. BRAND: Could you pull the mic a 

22 little closer.
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1 MR. INGLIS: At the moment you collect a 

2 piece of data, how do you bind the attributes to 

3 that data that essentially include, but perhaps 

4 some other things as well, what was the authority 

5 under which this data was collected? What are 

6 the burdens? What are the imposed constraints 

7 that come along with that? What are the 

8 proscriptions, if any, associated with that? 

9 And that should be atomically bound to 

10 that data element through its life, through its 

11 life of collection, processing, analysis and 

12 dissemination.

13 Now at some point there's going to be a 

14 second order use of that data where someone 

15 essentially reads a broad swath of material, 

16 synthesizes that in their head and then 

17 constructs a document across an air gap. 

18 That gets hard, but at least in that 

19 primary use of that data if you had a systemic 

20 view of it from start to finish, you make the 

21 auditor's job or the compliance oversight much, 

22 much easier. 
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1 And you therefore then in your system, 

2 in your technology essentially impose a 

3 constraint or a check every time something 

4 exercises privilege against that data, whether 

5 it's a collection, or analysis, or processing, or 

6 in dissemination. That makes the auditor's job 

7 much easier. 

8 And frankly, it has a nice deterrent 

9 effect on those inside the system because they 

10 know at every moment that they are held to 

11 account. 

12 But in my experience in government it's 

13 not so much the deterrent as it is the assist in 

14 an otherwise very, very rule-laden environment. 

15 A typical counterterrorism analyst at 

16 NSA would often deal with hundreds of constraints 

17 on the data sets that are available to them 

18 because various orders of the court, 

19 interpretations of the court, kind of sharing 

20 arrangements with various other nations would all 

21 come along with their independent assessments of 

22 how the data can or should be used. 
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1 So the bottom line is that technology 

2 can help us by essentially doing an atomic bind, 

3 right, meaning that it's organic to the data 

4 itself of what's its provenance, and that 

5 provenance should never be lost through the 

6 history of that system.

7 MS. BRAND: Thank you. I'd like to turn 

8 to the FIPPs, and Mr. Geiger, I was happy that 

9 you raised those, and Professor Cate as well. So 

10 I'd like to direct this question first to the two 

11 of you.

12 So, Mr. Geiger, I noticed that in the 

13 written statement that you sent to us, you talked 

14 about the FIPPs but you didn't really talk about 

15 the individual participation FIPP. 

16 And I guess when I talk about the FIPPs 

17 I'm referring primarily to the DHS version.

18 You said in your oral statement just now 

19 that the FIPPs are not a smorgasbord, they're a 

20 framework, you can't just pick and choose among 

21 them. And if that's the case and if you have to 

22 employ the individual participation FIPP, how can 
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1 that work in a surveillance context?

2 MR. GEIGER: So that is the toughest 

3 FIPP to apply in this context, absolutely. 

4 One way that you could do it, which is 

5 not politically viable and perhaps not even good 

6 policy, would be to loosen standing requirements 

7 on individuals to bring suit for violations of 

8 law. 

9 But my, I think, more reasoned answer is 

10 that if the individual participation FIPP is 

11 lacking in the national security context, then 

12 the rest of the framework has to work overtime to 

13 compensate. 

14 And that includes data minimization, 

15 which is why I emphasize collection limitations 

16 and transparency, as well as the rest of the 

17 framework. 

18 And I mean I absolutely recognize the 

19 challenges in applying individual participation, 

20 but this is one area, again, where government is 

21 different than the private sector and I think 

22 that difference should express itself in 
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1 particular in the data minimization principle.

2 MS. BRAND: Professor Cate, do you have 

3 thoughts on that? 

4 And I would ask also, there's been a lot 

5 written and said in public more recently about 

6 how perhaps the consent and individual notice 

7 FIPP really doesn't work very well in the private 

8 sector, either because nobody really understands 

9 what they're consenting to. Even if they 

10 understand it they don't have any other option, 

11 so they have to consent to get the service, and 

12 it's kind of a meaningless exercise.

13 Do you have thoughts on that and whether 

14 the individual participation FIPP can work in 

15 this context?

16 MR. CATE: Thank you very much. I do 

17 have thoughts on that, especially being one of 

18 the people who's written some of that.

19 I think the challenge of the FIPPs is 

20 that the they often lead us in the wrong 

21 direction. And I think this is a real challenge. 

22 I'm not in any way trying to make it sound easy 
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1 or make it sound like there's a simple answer 

2 here. 

3 But, for example, if we think about the 

4 FIPPs in sort of their classic 1980s OECD FIPPs, 

5 we're talking about notice and consent, we're 

6 talking about purpose specification, we're 

7 talking about use limitation to the purpose 

8 specified, and then we add things like data 

9 minimization and individual participation.

10 And frankly, almost all of these seem 

11 challenged in a modern data environment, private 

12 sector or public sector. 

13 In other words, how does that really 

14 work? You know, there are 60 people in the room, 

15 they all have cellphones, they have recording 

16 devices, they have video, they have audio, I 

17 don't have a policy statement from any of them. 

18 I don't know about my individual participation 

19 rights. I suspect they would look down on my 

20 wanting to interview them each about it. 

21 It's not a meaningful way to approach 

22 the issue. The issue is an important one, which 
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1 is how to protect privacy. But shifting the 

2 burden to the individual, which is what the FIPPs 

3 have the larger effect of doing, is a very 

4 difficult way to approach that. And I think it's 

5 an impossible way to approach it in the public 

6 sector environment. 

7 But it also may lead to completely wrong 

8 results. In other words, one of the surprising 

9 things to me, and I can't believe I'm going to 

10 say this in a place that's being recorded, but 

11 about the Section 215 was that the NSA collected 

12 all this data and did so little with it. It was 

13 astonishing. 

14 And so you would like to say, you know, 

15 when people talk about atomically binding the 

16 limits on what you can do with the data with the 

17 data, I'd like to think if we thought of 

18 something new we might do with the data that 

19 might really have a major effect on national 

20 security, we'd have a process for some sort of 

21 risk analysis, what's the benefit, what's the 

22 risk, what are the processes in place to protect 
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1 it, now let's do that thing. 

2 In other words, data has real value. It 

3 does in the national security environment, it 

4 does in the private sector environment. 

5 And I think we need to be thinking about 

6 approaches here that aren't binding everything to 

7 some mythical transaction that took place at 

8 which in the FIPPs world we say the individual 

9 agreed to this, even though I can't think of a 

10 case in which the individual actually agreed to 

11 it or it was meaningful consent. 

12 And then in the national security world 

13 we just overlook that. We just say, well, we've 

14 agreed for the individual because we think it was 

15 important, without again doing a clear and well16 documented type of risk assessment using clearly 

17 articulated values and harms, benefits and harms.

18 MS. BRAND: Go ahead.

19 MR. GEIGER: If I can just make three 

20 additional comments on the FIPPs. One, so it 

21 does sometimes lead programs in the wrong 

22 direction. 
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1 It is a useful framework for evaluating 

2 privacy protection, but the application of the 

3 FIPPs, what you're actually doing with the 

4 program, you may pass muster under your privacy 

5 impact assessment, but the actual the way the 

6 program is conducted on the grounds may not in 

7 fact be privacy protective. 

8 So I don't think that the FIPPs are a 

9 silver bullet, but the principles themselves I 

10 think are very useful for the evaluation of the 

11 program.

12 Second, there's been a long-standing 

13 controversy about notice and consent being 

14 inadequate, but that is why I said at the outset 

15 that the FIPPs is a framework. I mean each 

16 principle is dependent on the other. 

17 This came up very clearly in the 

18 healthcare context. People don't know what 

19 they're consenting to when they receive a notice 

20 from their doctor. They don't know what the 

21 HIPAA privacy notice really says or means, or 

22 what HIPAA does, which is why there has to be a 
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1 lot of additional privacy protections in place to 

2 actually meaningfully protect that individual's 

3 privacy.

4 And then lastly, FIPPs are not the only 

5 framework. I think that it is a very useful, I 

6 think it's an indispensable framework, but there 

7 are certainly other frameworks that can be 

8 applied and should be applied to the evaluation 

9 of security or data collection programs writ 

10 large.

11 MS. BRAND: Although this was the 

12 subject of the first panel today and not 

13 necessarily this panel, I want to ask about it 

14 anyway. So apologies if I'm springing this on 

15 you.

16 But I'd like to give you all a chance to 

17 give any views you might have on privacy, what is 

18 privacy, the sort of nature of the underlying 

19 privacy right.

20 And Mr. Inglis in particular, when you 

21 were at the NSA, I assume you spent some of your 

22 time thinking about how to protect privacy and 
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1 civil liberties and as you were doing that, what 

2 did you think that meant? What privacy interests 

3 were you trying to protect?

4 MR. INGLIS: I would say I don't think 

5 that has changed over time, though the technology 

6 might hold that at risk in different ways and 

7 there might be some downstream consequences, 

8 given the scope and scale. But the fundamental 

9 question always comes back to two things.

10 One, with respect to the perspective of 

11 the individual, is there a reasonable expectation 

12 of privacy for, fill in the blank what that 

13 information might be. 

14 That's the stuff of great legal debate, 

15 but operators think about that as well, 

16 particularly the operators inside the government 

17 because they're constrained by the Tenth 

18 Amendment to think about what is it they're 

19 actually authorized to do, everything else then 

20 being proscribed. 

21 But the second way to think about the 

22 issue of privacy is then what might you learn if 
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1 you take these discrete data sets and combine 

2 them in a way that might then give you some 

3 insight into things that were not self-evident 

4 from any one of the discrete data sets. 

5 You have to therefore think about the 

6 problem in the aggregation, synthesis downstream. 

7 Again, you might have some thresholds there that 

8 you have to think your way through that you don't 

9 want to go beyond at that particular point in 

10 time. 

11 I would tell you that at the National 

12 Security Agency ethos is as important as the 

13 compliance rules, the FIPPs mechanisms and things 

14 of that sort. Absent ethos, absent the art, 

15 right, the science will lead you astray and 

16 you'll essentially get into a place where science 

17 alone cannot help you essentially navigate the 

18 challenge, the question of how do you achieve 

19 both security and privacy in a world where they 

20 are massively converged in a place called the 

21 Internet.

22 MS. BRAND: Professor Cate, do you have 
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1 a thought on the nature of privacy?

2 MR. CATE: I was afraid we might run out 

3 of time before you got to me on this. 

4 I would say two things. One, this is an 

5 area where I think public sector versus private 

6 sector is a really important distinction and I 

7 think it has to be kept clearly in mind. 

8 In the private sector I think of privacy 

9 mainly in terms of, if you will, harms or impacts 

10 on individuals or on groups of individuals. 

11 So whether that's the way we think about 

12 it in the Fair Credit Reporting Act, like a 

13 higher price for credit or denying someone a 

14 benefit, or whether it's some other way in which 

15 we think about an individual being manipulated or 

16 being driven to pay a higher price or what have 

17 you.

18 In the public sector I think that is 

19 also true. I think all those specific impacts, 

20 those harms, if you will, although I don't mean 

21 to limit them to physical or financial harms, are 

22 present as well. 
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1 But I think there's probably something 

2 more in the public sector, which is privacy, from 

3 I think the very beginning of the constitutional 

4 debate, was seen as something about the balance 

5 of power between the individuals and their 

6 government, between the citizenry and the 

7 government.

8 There is something quite striking, and 

9 this I completely agree with Harley about, the 

10 more the government knows about individuals, the 

11 greater the risk that that information will be 

12 used in a way that alters that balance of power, 

13 that makes the government more powerful and makes 

14 the individual less powerful.

15 And it's, you know, a widely observed 

16 but an ironic twist as we've gone into the 

17 twenty-first century, we've in many ways gotten 

18 less transparency to the citizen about the 

19 government and more transparency about the 

20 citizen to the government. 

21 That is a clear alteration in that 

22 relationship, that power relationship or that 
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1 oversight relationship. 

2 So in that sense that's why, again, 

3 whether one focuses on collection or use it may 

4 be a not so significant matter, but I think at 

5 the end of the day it is use that matters. It's 

6 knowing how can the government use this 

7 information in a way that might affect me, as 

8 opposed to is the information out there, which 

9 seems to almost be the answer is yes now.

10 MS. BRAND: Mr. Grant.

11 MR. GRANT: I don't have necessarily the 

12 answer, but I think I have sort of a framework 

13 for thinking about it, which is to start to think 

14 about it from the perspective of social media 

15 right now because I think in that space you're 

16 seeing how, especially younger people, are 

17 viewing privacy.

18 If you ask, so most of the engineers at 

19 Palantir are, they appear to be about 14, and we 

20 had some discussions internally about sort of our 

21 own information security policies and should the 

22 company be able to look at social media like 
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1 LinkedIn, Facebook, publicly available 

2 information, but look at it as part of our own 

3 inside policy. There are ways to detect phishing 

4 and things like that using this kind of data.

5 And they vigorously objected to their 

6 own employer looking at that data, again, for a 

7 reason of information security. 

8 So it was interesting to explore with 

9 them and to say, but you tweeted, you tweeted 

10 that, which means people are going to read that. 

11 It is a tool for communication to the world. 

12 And they still felt, yeah, it is 

13 publicly available, anybody can Google it, but 

14 they still have an objection to government 

15 collecting it, or even government reading it, and 

16 then their employer reading it and things like 

17 that.

18 So I don't know exactly what that means 

19 in terms of coming up with a final definition of 

20 privacy, but it suggests that people, there is a 

21 different view of it. And that even public 

22 information, there's still privacy inherent in 
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1 public information somehow. 

2 And like I said, I think talking through 

3 sort of attitudes towards social media and 

4 understanding that could help us figure out what 

5 is this, the newer conception of privacy in this 

6 technological age.

7 MS. BRAND: Did you have something to 

8 say Mr. Geiger?

9 MR. GEIGER: Sure. I mean, I said most 

10 of it during my opening remark. I mean I do view 

11 privacy in the lens of control. I view it as an 

12 individual's ability to control information about 

13 herself, but then also the control that the 

14 entity holding information can exercise over 

15 individuals. 

16 I think it is very important not to just 

17 look at privacy harms, or privacy interests, or 

18 the extent that privacy can translate to control 

19 over a individual or their decisions in the 

20 context of today's technology. 

21 I think that it's very important to try 

22 to look out the next couple of decades and sort 
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1 of see what is coming down the pike. And there 

2 are some very pervasive, very privacy intrusive 

3 technologies that are, that I think we will see 

4 in our homes and maybe even in ourselves in our 

5 lifetimes and certainly in our children's 

6 lifetimes. 

7 And the laws have absolutely not kept 

8 pace, and without a change in the law, again, I 

9 reiterate that internal protections on use and 

10 access, while important, are not sufficient 

11 because they can change. They have changed. 

12 When we talk about protecting privacy, I 

13 think that we should be looking, as I said, to 

14 what we are protecting several generations down 

15 the line.

16 MS. BRAND: Just to get back to the 

17 topic of this panel again, Professor Cate brought 

18 up use restrictions. We've been talking about 

19 that throughout.

20 We're focusing on how the private sector 

21 might have solutions that the government might 

22 learn from. Private companies are obviously 
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1 doing something to control the use of information 

2 they collect. They have to. They have a privacy 

3 policy that says what they're going to do with 

4 your information and they have to comply with it. 

5 Are there mechanisms that the private 

6 sectors has used for enforcing their use 

7 limitations that are particularly effective that 

8 the government might learn from?

9 Mr. Grant, do you have a view on that? 

10 MR. GRANT: So we see this a lot 

11 obviously in terms of, so we, ourselves, don't 

12 hold data but our customers hold data, and trying 

13 to help them implement compliance.

14 Honestly, actually, they use the same 

15 basic mechanisms that I described in my testimony 

16 and often they have the same basic weaknesses. 

17 You know, do they have the 

18 infrastructure to manage access control to the 

19 granular level? A lot of them do not because it 

20 costs money and it takes time.

21 Are they conducting the oversight of the 

22 data? Probably more so than some people and 
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1 possibly the government, again, because of 

2 limited resources. But they're still probably 

3 not doing it at the level that you would hope.

4 One thing I notice is that a lot of 

5 them, there is, even in Europe where you have 

6 more commercial privacy law and more commercial 

7 privacy compliance requirements, a lot of times 

8 it's best guess.

9 So, for example, one that we've been 

10 running into recently now is looking into 

11 cybersecurity and information security, data 

12 exfiltration risks in the private sector. 

13 And in these giant, multinational 

14 companies they're trying to deal with employee 

15 privacy laws that are all over the map. 

16 And they're asking questions like, if a 

17 German employee sends an email to a U.S. 

18 employee, what privacy rules apply to the content 

19 of that email? 

20 In Germany you have to actually tell the 

21 employee, I'm about to start monitoring your 

22 email. In the United States you can pretty much 
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1 do whatever you want with a few exceptions. They 

2 don't know what the answer is so they make their 

3 best guess. 

4 So I think there are interesting lessons 

5 in terms of what the privacy is trying to do, but 

6 I actually think they're facing a lot of similar 

7 problems that are related to scale, that are 

8 related to lack of understanding of what the 

9 rules should be, as the government.

10 MS. BRAND: Anyone else have a thought 

11 on that question? 

12 MR. INGLIS: If I can add to it. So my 

13 own sense is that there's probably a lot of great 

14 technology out there that can be used, but any 

15 technology can fall short of your expectations if 

16 you don't use it in the right process, and 

17 therefore, we ought to give as much time and 

18 attention to process within which that 

19 technology might be used as the technology 

20 itself. 

21 In the following process it might be 

22 useful to consider that first and foremost before 
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1 you acquire any capability, whether it's in the 

2 government or within the private sector, you 

3 think your way through the necessity 

4 proportionality considerations, you know, is this 

5 necessary and have I done this only to the degree 

6 that it is necessary. 

7 And then what we're trying to achieve is 

8 not simply the balance between security and 

9 privacy, but transparency is the third leg of 

10 that stool. And absent transparency, you often 

11 find yourself in a place where people don't 

12 believe that you achieved the right balance of 

13 the first two.

14 That then derives, you know, the 

15 possibility in the government the need to 

16 essentially acquire explicit authority, which 

17 always comes with constraints, constraints are 

18 bound to that, and some measure of accountability 

19 for those constraints. 

20 The process elements that then are 

21 essentially implemented to pull that off, I think 

22 should have the aspect of continuous compliance, 
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1 not discrete compliance at various phase points, 

2 but continuous compliance. You think about it 

3 all the time, first, middle and last. 

4 Kind of a stretched analogy is part of 

5 the problem with the absence of cybersecurity in 

6 so many environments is you think about that as a 

7 bolt-on. Until such time as we build our 

8 systems, operate our systems continuously with 

9 that foremost in mind as the primary attribute, 

10 it'll break our hearts.

11 The second process element of 

12 implementation is an external component. 

13 Internal components are really essential. You 

14 have to hold the people accountable internal with 

15 the system. 

16 But unless there's an externally imposed 

17 accountability mechanism, you can wind up with 

18 mismatched expectations or the system might, in 

19 fact, go rogue. 

20 And then three, there has to be at 

21 various phase points required reporting, which is 

22 important because that then forces some 
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1 synthesis, some kind of retrospective that says, 

2 how do we actually aggregate our experience in 

3 this to come to some conclusions.

4 So is it meeting our expectations? Is 

5 it working as it should? Are we a little bit 

6 right of the course, left of the course such that 

7 we actually need to invest some time and energy 

8 in the process itself? 

9 Absent that, you find that you're the 

10 frog in the beaker and it's just getting a degree 

11 hotter moment by moment, all of a sudden you're 

12 the boiled frog. And you hadn't realized because 

13 you didn't step back and take hard look at it 

14 that you actually got off course a little bit 

15 some time ago.

16 MS. BRAND: Thank you, and I think my 

17 time is up, so we'll start with Mr. Dempsey and 

18 go down the line.

19 MR. DEMPSEY: Thank you, and thank you 

20 to the members of the panel for giving us your 

21 time today.

22 In a way building off of something that 
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1 Chris Inglis said, or at least that I heard you 

2 saying that we need the technology controls, we 

3 need to build the technology in a way that 

4 implements these controls, but at the same time 

5 we need policies that surround them. You need 

6 the legal rules, etcetera.

7 I think, John Grant, my first question 

8 to you, you talked a lot about the potential in 

9 terms of tagging information, and audit controls, 

10 and permission controls are very granular, but 

11 just to state the obvious, that's not a 

12 substitute for legal rules and policies.

13 MR. GRANT: Absolutely not. We try to 

14 say, you know, even when we talk about our 

15 privacy enhancing capabilities and stuff, if you 

16 think you're buying a switch that you can flick 

17 that protects privacy, it's not going to happen. 

18 It's not possible.

19 You have to be able to respond 

20 dynamically to changing situations. You have to 

21 be able to make human-driven nuance decisions 

22 about data and about how it's used and is it 
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1 being used appropriately. And that's just 

2 something machines can't do. 

3 And it's the same reason we argue at 

4 Palantir that you can't build a find terrorist 

5 button, that you need a human at the top of the 

6 decision-making chain and at the top of the 

7 analysis chain to do it.

8 And so I distrust any technology that 

9 says don't worry about it, we've got privacy 

10 covered. And so what the goal should be for 

11 technologists is, what kinds of tools do policy 

12 makers need and then the oversight officers, the 

13 oversight boards, and the civil liberties 

14 protection officers and things like that, what do 

15 they need and what makes their job easier or 

16 possible, especially when you're dealing with 

17 data at scale. 

18 So an easy example is there's a lot 

19 work, a lot of research going into improving 

20 access control interface. When you're dealing, 

21 with terabytes of information in the 

22 cybersecurities space, how can you create 
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1 technological shortcuts to allow a human to make 

2 the decisions about how to manage that data? 

3 And that's how you do it. You think 

4 about how do you support the policy, not how do 

5 you replace the policy.

6 MR. DEMPSEY: Let me go to Fred. Fred, 

7 totally accepting your point about the 

8 limitations of the FIPPs and totally accepting 

9 your point about the importance of focusing on 

10 risk and focusing on use, you're not saying that 

11 collection is irrelevant, that obviously the 

12 Fourth Amendment is in some ways a collection 

13 limitation. 

14 And that, you know, in a commercial 

15 context that company that had the flashlight app 

16 that was out collecting data, nobody even got to 

17 the harms analysis, that collection was 

18 inappropriate in and of itself.

19 MR. CATE: Right. You are absolutely 

20 right and I agree completely. In other words, 

21 I'm not suggesting collection is irrelevant, I'm 

22 suggesting we've made collection too much of the 
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1 end of the story, so that once you cross, you 

2 know, it's like a spillway in a dam, once you're 

3 over the collection limit, then anything else 

4 goes.

5 MR. DEMPSEY: Well, the ironic thing is 

6 that at NSA, as Chris Inglis said, their view is 

7 they never thought of it that way, that they 

8 thought that you have your collection 

9 authorization which is critical, your retention, 

10 your use, your dissemination, your retention 

11 limit, that each one of those --

12 MR. CATE: But if I can just respond to 

13 that. I think there's something of a mismatch 

14 here. And I'm not in any way doubting either 

15 what NSA was doing or what Chris is saying.

16 But one of the astonishing things, for 

17 example, when I read the Section 215 report that 

18 came out from the NSA's civil liberties office, a 

19 well-written report, it was full of all of the 

20 limits on what they were doing and the incredible 

21 what can only be described as bureaucracy around 

22 that, both technical bureaucracy and human 
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1 bureaucracy.

2 But it sort of ignored the fact, which 

3 is what I think has struck most of the American 

4 people, is how was the authorization obtained in 

5 the first place? 

6 You know, we had a law that said 

7 relevant to a specific investigation, you know, 

8 99 out of 100 people through relevant to a 

9 specific investigation meant, might be focused on 

10 specific individuals. 

11 Apparently the 1 out of 100 who didn't 

12 was a FISA judge, and then had other judges there 

13 along with him, and apparently some members of 

14 Congress.

15 So I think one of the critical issues 

16 when thinking about going forward is if this were 

17 the private sector there would have been 

18 immediate customer feedback. 

19 You know, if that were Facebook 

20 interpreting that to say, by the way, you know 

21 under that privacy policy that says we'll only 

22 collect data for limited purposes, it means that 
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1 we're going to collect absolutely everything, and 

2 then there would be customer reaction.

3 What do we create that will mimic that 

4 in the classified environment, in the 

5 intelligence environment? Maybe that's the 

6 PCLOB. I mean maybe that's literally having the 

7 outside of the agency but focused on privacy and 

8 civil liberties that says we understand the 

9 challenge but we think you've got the wrong end 

10 of the stick.

11 But I think it is being overly focused, 

12 for example, on the Fourth Amendment that creates 

13 this problem. As you well know, the FISC just 

14 dismissed the Fourth Amendment issues by saying, 

15 well, third-party doctrine, there's no problem at 

16 all. Let's go ahead. 

17 And somebody should have been saying, 

18 wait a minute, you're talking about collecting 

19 data on everybody. And then that would have 

20 focused the discussion in a way that all of the 

21 technological controls and all of the 

22 bureaucratic controls that have been now well-
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1 documented in the agency, somehow never did.

2 MR. DEMPSEY: That's very helpful. I 

3 don't want to further rehash 215, the history of 

4 215, and anyhow I have a red card so I guess 

5 that's the end.

6 MR. MEDINE: So let me just follow-up 

7 quickly on that point. Maybe what we need to do 

8 is supplement the FIPPs with the OMG standard, 

9 which is, you know, in private practice I would 

10 have a client and I'd say, everything you've 

11 proposed to do is perfectly legal, but are you 

12 nuts? 

13 I mean how do we embed that stepping 

14 back and saying, okay, the lawyers have 

15 technically signed off, everyone has technically 

16 signed off, but this is a crazy thing to be 

17 doing? 

18 MR. CATE: Well, I mean I think one 

19 positive step is adding someone like Becky 

20 Richards and an office to support her within the 

21 agency. I think that's one way. 

22 So you get people who aren't just 
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1 thinking about the law, but rather people who 

2 will say, I understand legal clearance is taken 

3 care of, but I still have the oh, my God 

4 response. 

5 Are you allowed to refer to God at a 

6 PCLOB hearing?

7 MR. MEDINE: It's free speech.

8 MR. CATE: I feel very nervous about 

9 that. 

10 I think the PCLOB is another way. In 

11 other words, you say we're going to have some of 

12 those similar roles, not by any means identical, 

13 but outside of the agency.

14 I think this is where I would say, 

15 although this just may reflect my naivete, you 

16 know, I would like to think that although we 

17 certainly need to have secret operations, we 

18 wouldn't have secret law. 

19 And so if a law that said one thing was 

20 being interpreted to mean the opposite, that 

21 someone would feel the need to signal that, as 

22 opposed to going out of their way to continue to 
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1 say, no, it doesn't mean what we actually think 

2 it means, and it means what only you think it 

3 means.

4 And so that we would build in avenues 

5 for transparency about the law, so that at least 

6 we all knew what the rules were going into it. 

7 And I think that's a huge problem when 

8 the law itself is effectively classified because 

9 of the way in which the interpretative process 

10 works.

11 MR. MEDINE: Sure, John.

12 MR. GRANT: Can I just jump in on that? 

13 How we embed that in the private sector, or 

14 certainly in our company, and it goes back to my 

15 point about education. 

16 Engineers and technologists think of 

17 things in terms of does it work or does it not 

18 work, and they just want to make things more 

19 efficient. 

20 But it's not because they don't care 

21 about privacy and civil liberties. They end up 

22 living in the world they create. It's just they 
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1 don't realize that this raises an issue. 

2 So if you improve education across the 

3 board so that the technologists throughout the 

4 NSA and throughout the private sector that are 

5 building the capabilities and things like that, 

6 if they're all conscious of privacy and civil 

7 liberties, they're going to raise these questions 

8 too. They're going to say, what are we building? 

9 And especially technology is an 

10 interesting place because it's the place where 

11 the engineer, the lowly engineer is more powerful 

12 than the CEO, because if the engineer says, I'm 

13 not going to build this, then that's it. And if 

14 the CEO says, I'm going to fire you, they say, 

15 okay, I've got four more job offers to go 

16 somewhere else. So there's a really interesting 

17 power imbalance there within the organization. 

18 So if you instill the values that you're 

19 looking for throughout the organization in the 

20 people, that's where you're going to get the OMG 

21 response.

22 MR. MEDINE: I have a question for 
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1 Harley and Chris. In our 702 report we noted 

2 that most of the information that was collected 

3 wasn't reviewed and therefore wasn't minimized, 

4 and that even of the information that was 

5 collected oftentimes it wasn't minimized in terms 

6 of being deleted because there wasn't a 

7 determination about whether it had foreign 

8 intelligence value. Harley proposes doing the 

9 minimization up-front when it comes in. 

10 So I have a question for each of you. 

11 One is, Harley, is that a practical matter given 

12 how much information is coming in?

13 And I guess to Chris, if that's not a 

14 practical way, how do we do minimization better?

15 MR. GEIGER: First, an unsolicited 

16 answer to your first question, which is in 

17 addition to the proposals that have just been 

18 discussed I think a FISA court special advocate 

19 would also help with the OMG standard. 

20 I think that it's a multi-layered 

21 solution having privacy and civil liberties 

22 offices in agencies, a PCLOB and a FISA court 
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1 special advocate hopefully gets us there.

2 In terms of whether front-end, so what I 

3 had said was that front-end minimization and 

4 back-end minimization are important. And so I 

5 actually, one of the things that I had said was 

6 that the determination ought to be made whether 

7 the information was needed and then flush it as a 

8 default unless that determination is made.

9 This is different than the way that I 

10 think it's done, at least in some agencies where 

11 they keep the information unless they make a 

12 determination that they don't need it, which is 

13 very different. And that sometimes causes 

14 information to languish. I think that that 

15 should be flipped. 

16 In terms of front-end information data 

17 collection, I do think that it can be feasible, 

18 but it also depends on the specific program, it 

19 depends on the purpose. 

20 And if the purpose is we're going to 

21 collect everything, that sets off the OMG 

22 standard for me. 
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1 But if the purpose is narrower, and I 

2 think generally speaking it should be, then yes, 

3 there should be data collection limitations. 

4 I understand that there are technical 

5 limitations there and that depending on the 

6 actual means of data collection, sometimes it may 

7 be unavoidable that you collect more than you 

8 need, but then you should be flushing the 

9 information that you don't need.

10 MR. MEDINE: I'm probably going to run 

11 out of time, so Chris, if you have any reactions 

12 to that?

13 MR. INGLIS: Yes, so on both parts, so 

14 the question of 215, I know we don't really want 

15 to rehash whether that's good or bad policy, but 

16 from an NSA perspective three branches of 

17 government participated in the creation of that 

18 program, sustainment of that over years time, 

19 multiple administrations, more than three dozen 

20 judges.

21 And so from an NSA perspective, charged 

22 to essentially effect the will of government, 
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1 short of a referendum amongst 315 million people, 

2 which we do every two years, I don't know how you 

3 actually kind of make a significant change in 

4 terms of how the government comes to some of 

5 those conclusions. 

6 The PCLOB is an extremely valuable 

7 addition, but you know, I think that we're always 

8 going to find ourselves in a place where 

9 stakeholders stand in the shoes of those they 

10 serve.

11 With respect to your specific question, 

12 it's problematic on a couple of counts. You 

13 know, first and foremost, if you try to minimize 

14 at the point of collection you then ironically, 

15 paradoxically begin to focus on things that you 

16 shouldn't. 

17 The strange truth in the world is that 

18 there are two ends of every communication in the 

19 world, sometimes more, right, if you add in the 

20 courtesy copies and the blind courtesy copies. 

21 And if your interest is legitimately in 

22 party one and you begin to then focus on party 
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1 two, right, who is involved in that conversation, 

2 without merit, without some reasonable or 

3 probable cause, you then begin to encroach upon 

4 their expectation of privacy, absent some kind of 

5 reason to do so. 

6 So the policy at this moment essentially 

7 uses this, upon recognition, which isn't a sloppy 

8 policy. It just says do not focus undue 

9 attention on that, and when you do encounter 

10 someone who deserves further protection, take it. 

11 You must take it. 

12 Built into that then are some time 

13 limitations for how long you can hold that data, 

14 and some necessity and proportionality conditions 

15 that say how much data is enough, for what 

16 purpose, and how long are you going to keep that 

17 without some meritorious reason. 

18 So if it participates or contributes to 

19 a report you keep that for longer. If it 

20 doesn't, then there are time limitations, you age 

21 it off. And those are always prescribed by those 

22 who essentially grant us our authority.
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1 MR. MEDINE: Thank you.

2 MS. BRAND: Ms. Cook.

3 MS. COLLINS COOK: So following up 

4 actually on a phrase that's been used a number of 

5 times today and asking the same question I asked 

6 a previous panel, there's this notion of 

7 reasonable expectation of privacy. 

8 To the extent that that evolves over 

9 time, which I think that it does, how does one 

10 ascertain what is a reasonable expectation of 

11 privacy? 

12 Is it based on a Washington Post poll 

13 that 50 percent of Americans are uncomfortable 

14 with X, Y or Z? Is it the conduct that 

15 individuals nonetheless engage in, that they're 

16 uncomfortable about communications surveillance 

17 but people still use their phones, they still 

18 engage in the world? 

19 If we were going to look to reasonable 

20 expectation of privacy as a touchstone, how 

21 should we ascertain what it is?

22 This is a question for the panel. 
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1 Chris, I'll start with you because you had 

2 indicated that the NSA did look to reasonable 

3 expectation of privacy as one of their 

4 guidelines.

5 MR. INGLIS: First and foremost, there's 

6 a basis of law which doesn't, if the technology 

7 changes over time give us, say, a free pass to 

8 say because the law allowed us to use the old 

9 technology in this way, the new technology, which 

10 is more intrusive, can simply just continue 

11 unabated. 

12 But there is a wide practice of law and 

13 the NSA considers that, you know, as it makes its 

14 appeals for authorities, which are always 

15 conditioned upon a Department of Justice 

16 representation and the right authority, either 

17 under 12333 or the courts. 

18 Second, there is an expectation at a 

19 place like NSA that you think through the eyes of 

20 those whose privacy would be encroached upon, 

21 right. So you think about what's the expectation 

22 of the individual and is their expectation such, 
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1 regardless of what the law might say, that this 

2 is something that deserves some aspect of 

3 privacy. 

4 And that necessarily then has to inform 

5 the conversation about what authorities you seek 

6 and what then provisions you seek those 

7 authorities for. 

8 Interesting dialogue earlier about the 

9 215 program and the internal bureaucracy. At NSA 

10 we thought that was a feature, right, that the 

11 court essentially proscribed use of that database 

12 for anything but the very surgical and narrow 

13 application of it. 

14 The sense at NSA was, is that if we had 

15 even requested to use that for other purposes, 

16 say, domestic terrorism, which is not our 

17 provenance, or say, weapons of mass destruction, 

18 rogue nations, that that would have been an 

19 encroachment into privacy that was excessive and 

20 therefore not meritorious right up front with 

21 respect to the possibility we might ask for that.

22 The program as designed was very 
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1 surgically, narrowly framed on something alone, 

2 which was warranted and justified under the 

3 concept of necessity and proportionality. 

4 And we had to avoid the creep beyond 

5 that because of an expectation based upon the 

6 consumer looking back at us, as to what they 

7 might think.

8 MS. COLLINS COOK: John, you also used 

9 the phrase as well I think here, and so if you 

10 have some thoughts on this.

11 MR. GRANT: The thing that jumps to my 

12 mind and it gets back into, again, when I was 

13 talking about analyzing data to sort of support 

14 the effects of this program, I think it's 

15 reasonable to expect that the government won't 

16 look at data that's not useful. 

17 That is a reasonable expectation of 

18 privacy, that the information that has not proven 

19 effective for some purpose, that that won't be 

20 collected and analyzed. 

21 And that's what we've been doing, as I 

22 said, rewriting our internal information and 
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1 security policies, and as we've surveyed 

2 everybody at the company they've said, I'm fine 

3 with you looking at some of this data, just tell 

4 me that it's useful, tell me why you're looking 

5 at it. 

6 Because of course they're interested in 

7 protecting our own internal information security 

8 at Palantir, and of course we're interested in 

9 protecting our own national security.

10 So I mean this isn't the only standard. 

11 Obviously utility can't be the only analysis 

12 point because there obviously are interests 

13 beyond that, but I think it's a significant 

14 question that we don't answer very well right 

15 now. 

16 And this is, you know again, across the 

17 board from that sector to the private sector, 

18 everybody wants data and they think they can do 

19 all of this stuff with data. 

20 And we get customers all the time who'll 

21 come in and say, I've got to understand the 

22 Twitter. And we'll say, well, what do you want 
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1 to know? And half the time, we'll say that 

2 information, if you want to understand do a lot 

3 of people like Justin Bieber or cats, then 

4 Twitter's great. If you want to understand more 

5 complex, nuanced theory, then maybe we should 

6 think about something else. 

7 And I think that government should do 

8 the same. And I think the government can answer 

9 those questions, again, looking at, analyzing how 

10 data is used and using that data about data. 

11 So to me that's one area where you would 

12 sort of expand that definition of reasonable 

13 expectation of privacy, which is it's reasonable 

14 to expect no one will look at data that isn't 

15 useful.

16 MR. GEIGER: The question you pose is a 

17 very difficult one. I mean courts are wrestling 

18 with it all the time. And everyone has a 

19 personal opinion about it, and so do I. I 

20 believe that reasonable expectation of privacy is 

21 a terrible framework actually.

22 The Fourth Amendment is supposed to 
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1 protect against unreasonable searches and 

2 seizures. The reasonable expectation of privacy 

3 is a judicial-made creation that has now allowed 

4 for some very unreasonable searches and seizures. 

5 Section 215 is a great example of that. 

6 Under the reasonable expectation of 

7 privacy framework, U.S. versus Jones 

8 notwithstanding, because I know that's kind of a 

9 mysterious opinion, but the Supreme Court seems 

10 to be sort of moving, inching along perhaps in a 

11 direction where they are doubting the reasonable 

12 expectation of privacy framework as it's been 

13 applied in the past several decades. 

14 But under current law would it be okay 

15 under the reasonable expectation of privacy test 

16 to have a network of drones or a network of 

17 ground-based cameras that watch everything that 

18 you do the moment you step outside of your house? 

19 I mean there is a very strong argument 

20 that, yes, that is okay under the reasonable 

21 expectation of privacy framework. 

22 So I think that it's the wrong framework 
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1 to be viewing a lot of this stuff. I think that 

2 it does not have to be left out of the 

3 conversation, just like the FIPPs, it is one 

4 framework. 

5 There should be multiple lenses, but 

6 none of them, including reasonable expectation of 

7 privacy, like the FIPPs, are going to be a silver 

8 bullet. And they're not going to provide you 

9 with a clear answer.

10 MS. COLLINS COOK: I think if I have 

11 time for one additional question, I'm still 

12 seeing yellow. 

13 So moving up the analysis of data and 

14 requiring agents or analysts to make an 

15 assessment of whether or not information is 

16 relevant or is necessary to maintain, rather than 

17 potentially letting that information simply age 

18 off of your system, what about the privacy 

19 implications of that type of approach, which to 

20 me, I have been unable to get past this notion 

21 that that would require agents or analysts to put 

22 eyes on more communications than they would 
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1 otherwise review. And so what is your answer to 

2 the privacy implications of that shift?

3 MR. GEIGER: I mean I suppose that there 

4 are two ways to do it. You could require the 

5 agent to look over every piece of data that 

6 they've collected. 

7 If the amount of data is small, which is 

8 my main point, I mean having, not data retention 

9 but collection limitation at the front-end. If 

10 the data population is small that is less of a 

11 problem. 

12 If you're requiring the agents to look 

13 through a large amount of data that you know 

14 contains information about individuals who are 

15 not connected to a crime or terrorism, that 

16 becomes more of a problem. 

17 Then on the flip side I suppose you 

18 could have the agent merely looking at data that 

19 they know is connected to other parts of their 

20 work.

21 I mean I don't think there's a hard and 

22 fast rule. It's going to be depend on the 
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1 program, it's going to depend on what the agent 

2 is looking for. 

3 For that reason I think that data 

4 minimization, again, on the back-end is not the 

5 answer. It has to be part of the framework. And 

6 collection limitation at the front-end is a 

7 crucial part of that framework.

8 MS. BRAND: Judge Wald.

9 MS. WALD: Whether or not you think that 

10 it's important to limit collection or you think 

11 perhaps you can wait a while or see and go after 

12 it more forcefully at the use end, I'm interested 

13 in what you think the role of the courts are. 

14 In our other systems like criminal 

15 justice, ultimately, and even under the Fourth 

16 Amendment, the courts are kind of the final 

17 analysis. And even in many of our civil 

18 regulatory systems ultimately they come up. 

19 So the question is two parts. At what 

20 stage, whether you believe in collection 

21 limitations or you believe more in use, do you 

22 think the internal, all of the internal audits 
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1 and various other techniques that we've talked 

2 about are not enough, that you need some kind of 

3 an outside look at it? 

4 But secondly, I think as a former judge 

5 I ask this question, if you were scared to come 

6 before -- and that is, do you really think that 

7 the limited role that the FISA court has been 

8 allowed to play in terms of the secrecy of its 

9 operations, and even with our recommendation and 

10 other people's suggestion about adding an 

11 adversary, and even some of the judges on that 

12 court, not only did they come out in different 

13 ways, all judges do, but they were frustrated 

14 themselves in terms of the technology sometimes. 

15 Judge Bates remarked that it was 

16 practically impossible, given all of the 

17 complexity of the technology we've talked about 

18 and the fact that these judges would come in from 

19 their regular work for a week at a time and then 

20 go back again, is that the best kind of outside, 

21 not outside surveillance, outside look, an 

22 independent look, or is there some better way to 
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1 get the notion of an independent, the Supreme 

2 Court always talks about independent and neutral? 

3 It's a big question. Go at it, starting 

4 with Professor Cate, any way you want.

5 MR. CATE: Thank you very much, Judge 

6 Wald. I would say I think the role of the courts 

7 is absolutely essential. I think the important 

8 feature of that role is it needs to be an 

9 independent role, and I think one of the concerns 

10 with the FISC is that as this set of opinions 

11 went back and forth and, you know, small 

12 modifications, and updates, and briefings and 

13 corrections, it involved the court in the more 

14 daily operation of the agency than I think we 

15 would typically think appropriate or desirable, 

16 that we really want an independent, neutral and 

17 detached court. 

18 The challenge of technology is huge for 

19 all of us. Even engineers have difficulty 

20 keeping up with the technology. I think there 

21 are, and we have seen some ways of dealing with 

22 it. One is court-appointed experts. 
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1 Another, as we saw in the Supreme 

2 Court's most recent privacy opinion this summer 

3 it cited heavily to amicus briefs from CDT, and 

4 from EPIC and others where they explained the 

5 technology and the impact of the technology, and 

6 the court clearly relied on them. And I think we 

7 shouldn't overlook that.

8 And then of course courts also have 

9 remarkable powers to compel the parties to 

10 explain the technologies in clear and 

11 understandable language and to not accept their 

12 filings or to not rule on their filings until 

13 they do.

14 So I could say more but let me share the 

15 microphone.

16 MR. GEIGER: I absolutely agree with 

17 everything Professor Cate just said. The courts 

18 play a very crucial role in the oversight of 

19 national security surveillance programs. I think 

20 that the court is constrained by a lot of 

21 statutory limitations. 

22 I think we would welcome, at least the 
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1 privacy advocacy community would welcome court 

2 oversight of minimization procedures and on the 

3 ground controls on privacy. 

4 I know the court does some of that, but 

5 I know that it is also limited to sometimes just 

6 a certification.

7 We have talked about having a special 

8 advocate. I don't necessarily view that person 

9 as an adversary because I think that in many 

10 cases the court and the government are also 

11 trying to protect privacy, they just maybe differ 

12 on the strength of that privacy protection. So I 

13 think that the special advocate could, in fact, 

14 be an ally. 

15 But then also technical experts and 

16 amicus. One of the problems that we're seeing in 

17 the debate over bringing in amici or bringing in 

18 a special advocate is that there are some forces 

19 in the court, perhaps formerly of the court, who 

20 would like to see greater restriction placed on 

21 those parties, so that it is the FISA court that 

22 instead gets to decide what role and what access 
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1 to information these amici will play, which will 

2 severely undercut their effectiveness and their 

3 ability to help the court. So I would urge 

4 resisting those calls.

5 MS. WALD: Don't you think, this is a 

6 follow-up just on the point you made, don't you 

7 think that in some cases, even the legal or even 

8 possibly constitutional reasonableness of 

9 something is dependent on understanding the 

10 technology of it? 

11 I mean I think Judge Bates felt that 

12 way -- 

13 MR. GEIGER: Certainly.

14 MS. WALD: In one of the cases that was 

15 declassified and put out that way. 

16 So you think that they are equipped to 

17 do that now, or do you think the advocate will 

18 fill that role, or do you need more?

19 MR. GEIGER: So I don't know enough 

20 about the judges to make a determination about 

21 their level of familiarity with technology. 

22 But I mean this technology that is 
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1 being exploited in some of these instances can be 

2 extremely complicated, and so, no, I would not 

3 imagine that most lawyers have that sort of 

4 training and so I think that there -- I know that 

5 the court already has powers to some extent. I 

6 think those should be loosened to bring in 

7 technical experts as amici to explain this in as 

8 clear a manner as possible, because I think 

9 you're absolutely right, technology does have a 

10 direct bearing on the rights that are being 

11 manipulated.

12 MR. GRANT: And so I'll just jump off 

13 that one. I think that it's critical to have a 

14 translator role for the court, someone to help in 

15 an unbiased way try to explain the technology. 

16 And you know, this isn't just an issue 

17 for the court, it's an issue for Congress. You 

18 know, I was trying to write cybersecurity 

19 legislation before I left and one of the 

20 challenges was you have to have a really complex 

21 technical debate and members are naturally going 

22 to be uncomfortable taking a strong stand when 
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1 they're not a hundred percent sure what the 

2 technological considerations are. And the end 

3 result is you sort of paralyze things. 

4 I think the critical question, so the 

5 court role is vital and it's important that it 

6 takes time because, you know, by nature that 

7 briefs out the issues and it helps you understand 

8 things.

9 The challenge is what are you doing in 

10 between. Because technology becomes ubiquitous 

11 even in a matter of months sometimes, and it 

12 starts to have a real effect on people's lives 

13 right away, and it's going to take 10, 15 years 

14 sometimes for the court to eventually settle on 

15 what they want to do. 

16 So what do you do in the meantime and 

17 how should people be guided? Should there be 

18 ethical limitations on what the private sector 

19 wants to do? Should the government figure out 

20 ways to sort of slow walk in technology? And 

21 what's the framework for making that decision and 

22 implementing that? I think that's the real 
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1 challenge.

2 MR. INGLIS: I largely agree with what's 

3 been said. I think that with respect to the role 

4 of the court neutral and detached is, I think, 

5 the right way with respect to their opinion on 

6 the efficacy of the policy or the government's 

7 representation. But they have to have a solid, 

8 if not exquisite understanding of the technology, 

9 and I would distinguish between the two. 

10 I think the role of an adversary and a 

11 technology expert at the court, you know, has 

12 great merit and would, I think, add to their 

13 ability to at least understand the technology.

14 And we have to hedge our expectations, 

15 not because the government wouldn't want to 

16 reform, but at NSA could be perhaps exhaustive 

17 about technology at some moment in time in its 

18 presentation to the court, but at best it can 

19 only be illustrative as to where that technology 

20 is going to go. Nobody knows where the 

21 technology's going to go. 

22 And the use of a certain technology, 
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1 even if the technology doesn't change, change is 

2 in and of itself. People make different use of 

3 technologies. 

4 And so forecasting that is, I wouldn't 

5 say a fool's errand, but it's really hard.

6 MS. BRAND: Thank you. We have a couple 

7 of public questions. We may only have time to 

8 get to one of them. 

9 But Professor Cate, I think this is 

10 directed at you. It says, if you don't like the 

11 FIPPs, what alternative do you suggest? 

12 MR. CATE: So first of all, to be clear, 

13 I'm not saying I don't like the FIPPs, I just 

14 don't think the FIPPs are the be all and end all. 

15 And second of all, I suggested risk 

16 management as a pretty useful tool as a way of 

17 identifying both potential negative impacts and 

18 also beneficial impacts. 

19 And you know, one of the things we 

20 haven't talked about is the value of the use of 

21 data for national security or foreign 

22 intelligence gathering or whatever. 
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1 And one advantage of a risk management 

2 approach is it helps focus on both sides of that 

3 equation. It helps drive towards specificity. 

4 So if you ever want a documented decision that 

5 reflects that analysis, it's one way to help 

6 focus attention on it. 

7 And then as we identify those potential 

8 harmful impacts, negative impacts, whatever we 

9 want to call them, we can then look for tools 

10 that help minimize those impacts. 

11 So if the harmful impact is if you 

12 collect all this data it might be stolen, we can 

13 talk about security. 

14 If we collect all this data and the fear 

15 is that the government might repurpose it for 

16 some other use, then we can talk about use 

17 limitations that would help address that.

18 But I think a great advantage of doing 

19 this is it makes clear in a way that the FIPPs do 

20 not, where should we be focusing our attention, 

21 whether we are academics, or the PCLOB or, you 

22 know, with the process within an agency.
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1 MS. BRAND: Okay, thank you. 

2 Mr. Chairman.

3 MR. MEDINE: Thanks again to the 

4 speakers on this panel and all the panels 

5 throughout the day, as well as the audience 

6 members who submitted questions. 

7 I think we've had a remarkably 

8 informative and thoughtful discussion. We've 

9 heard from academics, government officials, 

10 advocates, technologists in industry, which is a 

11 lot, and we've covered a broad range of topics, 

12 FIPPs, Fourth Amendment, collection and use, 

13 encryption, de-identification, oversight, 

14 accountability, technology, mosaic theory and 

15 bulk data all in one day. 

16 So you've given us a lot to chew on. I 

17 think this is very helpful for us as we consider 

18 how to move forward carrying out our mission to 

19 balance national security with privacy and civil 

20 liberties.

21 So unless any other Board members have 

22 any comments, today's Board activities are 
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1 complete. 

2 We encourage anyone who has comments, 

3 whether panelists, or members of the audience, or 

4 others to submit written comments. We're 

5 accepting comments on regulations.gov through the 

6 end of the year. 

7 A transcript, again, of this day's 

8 activities will be posted on our website, 

9 pclob.gov.

10 And with that, I move to adjourn the 

11 hearing. All in favor of adjourning say aye. 

12 (Vote taken.)

13 MR. MEDINE: We are adjourned. It is 

14 now 4:15. Thank you very much. 

15 (Whereupon, the hearing was adjourned.)

16 

17 

18 

19 

20 

21 

22 
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1 CERTIFICATION

2

3

4 I, LYNNE LIVINGSTON, A Notary Public of 

5 the State of Maryland, Baltimore County, do 

6 hereby certify that the proceedings contained 

7 herein were recorded by me stenographically; that 

8 this transcript is a record of the proceedings.

9 I further certify that I am not of 

10 counsel to any of the parties, nor in any way 

11 interested in the outcome of this action.

12 As witness my hand and notarial seal 

13 this ________ day of __________________________, 

14 2014.

15 ________________________________

16 Lynne Livingston

17 Notary Public

18 My commission expires: December 10, 

19 2014

20

21

22
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A

abandon 19:10

38:13

abandoning

288:5

abide 158:16

172:21

ability 44:17

48:12 111:2

121:1 161:3

165:9 195:13

228:20 239:15

266:4 340:12

377:3 380:13

able 23:11 41:11

64:5 89:14

90:19 135:12

146:2,2,5

155:5,6 166:8

166:10 170:11

170:19 181:8

182:8,10 183:3

184:9 188:22

190:1 195:6,21

202:18 205:17

208:11,15

221:8 270:16

270:18 279:15

285:21 304:14

307:1,2 309:22

310:9 311:22

312:2 317:7

338:22 348:19

348:21

ably 101:21,21

abroad 227:17

absence 173:21

346:5

absent 335:14

335:14 345:10

347:9 362:4

absolute 23:10

31:22 32:9

64:4 168:18

absolutely 42:18

84:14 132:21

146:16,19

151:4 152:3

177:21 194:17

254:22 284:2

287:15 313:17

316:12 327:3

327:18 341:7

348:13 350:19

353:1 374:7

375:16 378:9

absolutist 73:8

abundance

139:12

abundant

138:14

abuse 30:18,19

31:7 62:6 85:4

86:6 94:18

113:22 169:18

abused 55:16

abuser 114:2

academeia

234:13

academics 274:7

382:21 383:9

academy 181:15

313:10

accept 40:6 70:6

98:16 99:9

101:4 375:11

acceptance

213:7

accepted 95:8

97:5 106:11

149:19 204:8

accepting 350:7

350:8 384:5

access 45:4 75:9

78:11 93:2

104:19 112:15

115:6 127:14

127:15,19

130:6 131:7

132:4,18

146:10 153:16

162:13 168:6

170:12 188:20

191:5 195:21

221:12,15

233:8 241:6,7

249:15 293:5

300:10 306:4

307:17,18,21

307:22 308:1,1

308:4 310:13

341:10 342:18

349:20 376:22

accessed 94:5

190:20 306:22

accesses 39:9

168:1

accessible 75:1

162:5

accomplish

291:16 300:16

300:21

account 36:13

37:2,5 68:16

104:6 225:3

231:7 247:8

268:20 318:1

325:11

accountability

21:19 22:1

24:13,17 52:16

71:13 72:19

73:18,19 83:22

84:8 85:7 86:2

94:6 126:5

127:9 129:2

223:1 225:4

276:17 345:18

346:17 383:14

accountable

237:2 346:14

accumulating

33:4 139:17

accumulation

45:7

accuracy 93:14

93:17,19

accurate 41:9

48:15 84:14

93:9,12 94:1

158:21 159:12

accurately

18:22 99:3

achieve 86:4

114:17 289:2

313:22 314:3

316:16,19

317:11,12

323:9 335:18

345:7

achieved 345:12

achieving

304:13

acknowledged

95:16 123:10

132:8

acknowledge...

84:11,15

acknowledges

292:9

acquire 42:20

345:1,16

acquired 151:1

acquiring 323:6

acquisition

43:12 323:6

acromyn 220:2

acronyms 296:5

act 14:7 21:7

22:6,7,18

77:22 103:6,14

130:14 212:6

216:5 221:10

222:12 229:19

240:21 281:10

282:6 301:12

336:12

acting 107:11

208:22

action 28:1

69:19 281:21

298:12 385:11

actions 22:11,19

198:17

active 135:16

284:15

actively 113:5

144:3 145:20

161:16

activities 14:2

28:20 39:13

45:15 46:14,21

50:6 128:13

130:8 182:12

183:5 208:4,5

210:2,11

211:13,16

214:5,20

217:10 218:2

219:13,21

223:8 224:14

224:15 225:18

226:13 230:8

231:6 242:14

246:14 247:5

251:8 254:19

268:17 271:19

272:1 279:15

302:8 383:22

384:8

activity 5:1

20:18 21:3,10

31:2 39:11

53:17 65:3

70:22 183:13

212:5 218:11

233:1,4 245:2

248:18 260:22

262:4 265:7,11
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281:17

actors 94:21

107:15 228:7

308:20

acts 26:20

174:18

actual 154:7

156:4 291:19

322:12 332:5

360:6

adage 247:12

adapt 146:6

149:17

adapting 277:22

278:1

add 10:20

119:10 198:11

244:11 296:12

315:15 329:8

344:12 361:19

380:12

added 307:7

adding 60:12

354:19 373:10

addition 115:21

239:22 298:21

358:17 361:7

additional 12:18

81:9 154:13

181:9 185:7,8

236:18 258:9

270:19,20

274:10 331:20

333:1 370:11

additionally

129:9 228:19

addon 198:19

address 26:5

46:20 47:1,21

50:7 67:5

105:4,7 114:13

132:8 163:9

164:11 182:1

207:8 217:13

230:12 231:12

236:1 240:6,7

270:4 272:14

297:11 382:17

addressed 3:10

6:5 20:22

179:18 203:22

230:14

addresses 49:20

adequate 24:12

24:17 29:22

30:11 61:17

83:21 154:19

adequately

22:22 72:18

94:7 211:4

adequatly 262:2

adhering 125:15

adjourn 384:10

adjourned

384:13,15

adjourning

384:11

adjudicated

70:16

adjudicator

29:18

adjust 100:14

admendment

281:20

administration

44:15,20

128:18 222:6

282:7

administrations

360:19

administrative

29:21 30:9,12

59:3 95:7

administrativ...

281:7 282:10

adopt 145:9

adopted 100:4

adoption 125:8

adopts 12:1

advance 10:10

69:14,16 86:12

advanced 40:14

91:17 170:8

advances 27:5

226:15 228:21

advancing 77:5

77:6 187:16

234:4

advantage 12:18

317:17 382:1

382:18

advantages

38:18

adversarial

130:13 212:10

adversaries 89:6

208:14,16

267:11

adversary

135:16 280:7,7

373:11 376:9

380:10

adverse 56:3

60:7,18 62:4

adversity 55:9

advertised

295:11

advice 172:3

207:13 216:3

225:15 241:15

advising 124:19

advisor 25:6

215:13

advocacy 42:6

295:15 376:1

advocate 85:21

212:9 240:16

244:18 358:18

359:1 376:8,13

376:18 377:17

advocated 168:4

advocates 274:7

383:10

affairs 33:12

189:17

affect 20:1

202:9 212:13

312:2 338:7

affiliation 37:13

afraid 336:2

afternoon 203:4

203:7 284:22

afterward 38:21

age 25:15 74:21

168:7 186:6

297:9 340:6

362:20 370:17

agencies 39:3

40:13 41:4

74:2 81:9 91:1

114:10 133:1,5

205:9 208:14

208:22 211:10

212:14,17

241:7,17

244:19 248:20

264:20 267:8

298:5 299:17

306:11 319:14

358:22 359:10

agency 3:14

63:3 189:16

209:10,14

217:7 222:13

226:1 242:3

249:15 251:18

256:16 264:5

273:22 275:11

277:11,12

319:8 335:12

353:7 354:1,21

355:13 374:14

382:22

agencys 221:13

225:12

agent 23:6

281:17 371:5

371:18 372:1

agents 107:12

370:14,21

371:12

aggdom 217:20

220:4

aggregate 61:3

61:7 77:22

89:7,15 163:10

164:12 258:9

347:2

aggregated

77:15 129:17

236:13 323:10

aggregating

40:2 256:8

aggregation

47:4 48:2

50:17 61:15

77:3,14 78:1

161:3 258:7

335:6

ago 123:14

137:16 149:19

167:10 229:9

288:6 306:12

347:15

agree 8:9 31:13

50:11 52:2

65:22 77:8

78:3,13 84:17

87:10 89:16

96:20 97:1

98:3,10 108:2

125:21 146:13

149:13 186:16

189:9,20

281:22 296:15

313:17 314:6

315:9,10

316:11 337:9

350:20 375:16

380:2
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

3

agreed 6:19

331:9,10,14

agreement 97:4

97:19 100:8

129:16

ahead 51:2

201:21 202:2

331:18 353:16

aim 21:15

air 324:17

airbags 40:8

airplanes

294:19

airport 58:15

akin 26:16

alex 3:11 203:14

206:4,16,22

219:16 223:20

241:4 245:8

246:22 247:11

252:17 262:5

264:6 275:21

278:16 310:5

alike 137:8

alleged 130:20

241:9

allies 227:15

allow 91:17

292:10 298:21

298:22 301:16

350:1

allowed 78:21

243:20 355:5

364:8 369:3

373:8

allows 14:8

34:21 38:20

52:10,10

132:17 277:19

307:14

alluded 172:6

185:12

ally 376:14

alongside

226:15

alphabetical

109:18

alteration

337:21

alternate 30:12

298:4

alternative

381:11

alternatives

113:3

alters 337:12

altogether 32:7

alvaro 3:4 117:3

117:3,8 146:21

166:18

amassing

104:17

amazing 206:9

amazon 93:13

93:15

ambitious 7:22

amend 282:6

amended 221:15

amendment

12:20 14:22

27:22 29:13

42:2,14 43:6

52:10 53:7,14

64:13,22 70:1

71:15,17,22

72:6,7,8,21

80:19 81:8

82:1,2 103:18

107:5,9 121:5

121:6 167:14

167:19 168:19

221:12 229:16

292:6,13

293:14,18

294:3,5 314:15

334:18 350:12

353:12,14

368:22 372:16

383:12

amendments

87:22 314:12

american 16:12

131:6 163:14

168:2 197:15

214:6 252:19

278:18 352:3

americans

119:22 122:7

122:16 200:22

201:3 363:13

amici 376:17

377:1 378:7

amicus 375:3

376:16

amount 23:1

63:7 72:18

91:4 137:18

150:2 152:12

178:4 182:16

306:8 320:6

371:7,13

amounts 186:17

310:7,15 320:3

320:7

analogy 40:4

133:19 194:19

346:4

analysis 11:7

37:17 40:18,22

41:9 50:22

62:9,12 65:17

90:19 91:7,13

91:18 94:3

101:13 105:13

111:12 115:13

117:15 229:17

229:20 230:4

231:2 233:11

233:20 236:18

260:18 263:8

269:22 270:19

273:20 293:22

308:13 310:19

312:19 313:1

321:18 323:3

324:11 325:5

330:21 349:7

350:17 367:11

370:13 372:17

382:5

analyst 174:6

323:11 325:15

analysts 256:11

321:17 370:14

370:21

analytic 39:4

304:13 320:10

323:12

analytics 35:5

35:17 47:9

114:22 304:5

305:20

analyze 34:3

105:1 117:16

228:20 232:2

320:2,3

analyzed 37:11

366:20

analyzing 40:3

233:14 312:20

366:13 368:9

ancient 8:10

anecdotal 311:4

anecdote 310:17

anecdotes

312:20

anew 53:12

angeles 182:7

animate 98:14

animates 85:4

annie 3:3 110:12

110:18 165:18

176:1

announce

161:16

announced 4:9

132:7 157:1

anonymity 5:3

anonymization

113:6,13,19

159:4 162:7

175:16 196:9

anonymized

113:10 196:17

anonymous

198:12 269:16

answer 26:9,10

58:10 76:10

102:18 103:9

107:8 155:14

175:21 197:9

199:15 237:18

238:16 267:14

327:9 329:1

338:9,12 344:2

358:16 367:14

368:8 370:9

371:1 372:5

answering

141:15 230:5

318:15

answers 76:2

108:11 313:16

anticipate 58:11

181:17 282:18

antilock 40:8

antiques 25:22

antiterrorism

182:21

anton 3:3

110:12,19

116:17,22

142:8,21

144:11,18

145:14,17

146:16,19

159:2 160:1,13

162:1 164:16

166:5 175:17

176:17 177:21
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

4

184:7 185:3

186:22 187:1

194:7,11,17

195:19 198:11

199:14 200:13

antons 189:10

anybody 55:16

58:7 64:9 81:4

164:14 182:1

244:1 339:13

anymore 149:21

209:20

anyway 64:21

106:16 333:14

apart 256:4

apologies 33:10

333:14

apologize 10:10

app 350:15

apparatus 89:14

apparent 199:6

266:8 273:22

apparently

352:11,13

appeals 364:14

appear 6:19

338:19

appears 17:15

80:8

applaud 287:16

apple 112:3

applicable

182:20 216:4

223:21

application

106:18 299:20

303:3 332:2

365:13

applications

32:17 137:2

143:5 159:5

applied 6:7 72:1

333:8,8 369:13

applies 42:15

107:10 135:2

272:1

apply 46:1

64:11 75:16

80:22 81:11,14

82:9,13,16

86:18 113:7

114:3 119:15

162:17 163:3,4

187:14 191:10

196:10 202:14

208:2 220:1

260:7 271:9,10

271:18,20

272:4,8 273:1

276:1,10

297:16 299:16

320:1,2 327:3

343:18

applying 3:17

46:11 82:7,10

112:12 271:15

300:3 327:19

appreciate

25:11 116:3

132:6,21

134:21 165:16

215:8

approach 5:18

12:8 40:1

53:21,22 61:21

72:7 88:3

98:20 105:8

111:11 125:3

126:17,20

173:14,15

187:8 231:2,4

244:3 289:16

291:13 329:21

330:4,5 370:19

382:2

approaches 39:3

172:8,9 235:7

331:6

approaching

211:6 295:7

appropriate

25:13 32:19

71:12 73:1

94:5 168:13

169:6 258:20

261:3 374:15

appropriately

52:22 73:7

94:1 130:6

209:1 248:6

349:1

appropriation

5:9

approval 219:11

222:8 223:11

281:13 308:15

308:18

approved 222:7

314:22

arbitrary 102:5

architect 3:7

62:20 134:15

architecture

217:5 221:1

225:5

archive 306:16

archives 222:6

area 96:5

105:20 184:8

184:18 200:7

280:9,18

293:11 327:20

336:5 368:11

areas 46:14

69:19 192:9

276:5 294:9

arent 20:17

24:15 29:19

166:16 173:4

200:15 309:15

321:4 331:6

354:22

argue 63:3,9

89:10 118:19

317:9 349:3

argued 38:12

argues 150:13

arguing 129:11

argument 18:11

18:12 43:14

90:2 120:8

167:7 310:18

311:4 369:19

arguments 18:9

18:12 268:5

arises 29:1

arm 56:20

arms 68:18

298:18

arrangements

325:20

array 62:15

319:4

art 233:17,19

234:17 236:16

261:14 270:14

316:8 335:14

article 45:12

46:6

articles 54:3

195:13

articulable

60:13

articulated

272:3 331:17

articulating

204:3

arts 235:12

ascertain 363:10

363:21

aside 58:17

97:21 317:15

asked 157:9

216:10 219:22

239:3 363:5

asking 80:18

87:2 100:12,20

100:22 105:20

182:14 196:15

229:21 233:3

238:19 262:11

282:20 288:20

289:4 318:18

343:16 363:5

asks 41:2 104:5

aspect 48:10

58:4 236:15

345:22 365:2

aspects 8:9 11:5

15:19 42:1,5

45:16 54:12

55:2 59:11

101:12 199:18

211:15 248:13

249:3 251:19

315:11

aspersions

123:16

assembled

204:22

assert 112:11

asserting 191:20

assertion 27:4

assess 205:5

231:18 236:9

254:19

assessesment

218:20

assessing 245:12

245:15

assessment

221:20 223:13

231:14 232:20

233:6 261:12

290:5,18

331:16 332:5

370:15

assessments

204:10 219:13

220:13 223:9
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

5

224:21 240:20

241:1 325:21

assist 254:2

325:13

assistant 25:7

136:10

associate 124:22

associated 194:9

214:4 232:22

247:19 324:8

association 11:3

associations

16:19,19 153:3

assume 64:11

107:13 333:21

assuming 92:19

256:20

assumption

186:17 254:5

assurances

130:17 131:13

212:17

assure 55:15

182:14,15

astonishing

330:13 351:16

astray 335:15

atheist 140:18

atmosphere

54:18

atomic 326:2

atomically

324:9 330:15

attach 34:10

attack 141:8

161:16 170:13

170:20 195:5

attackers 112:1

attacks 111:16

112:15 138:1

141:3,6,10,12

169:14 170:4,7

attempt 7:21

130:17 212:12

attempted 282:5

attempting

11:14 174:7,11

attempts 68:20

131:8 212:4

255:17,17,18

attendance

16:19

attends 192:8

attention 5:4

95:11 150:2

155:15 157:4,5

158:6 243:8

286:6 287:12

292:2 293:8

344:18 362:9

382:6,20

attitudes 340:3

attorney 29:5,8

112:10 206:17

215:13,17

217:19

attorneys

237:12

attracts 95:11

attribute 346:9

attributes

323:19 324:2

audience 6:13

9:21 88:9

102:3 109:1,9

110:5,7 141:22

154:12 181:12

278:7,11,15

281:3 286:12

318:8,13 383:5

384:3

audio 329:16

audit 31:19 93:3

94:4 95:3

308:9 310:12

320:7,17,18

321:1,6 348:9

auditing 57:21

246:13 276:18

308:22 321:1

321:22

auditors 324:21

325:6

audits 56:8

372:22

authenticated

112:16

authorities

204:17,18

209:17 229:18

302:6,9 323:5

364:14 365:5,7

authority

107:11 219:20

242:13 301:20

314:17 322:18

324:4 345:16

362:22 364:16

authorization

351:9 352:4

authorize

302:12

authorized

112:16 131:7

265:1 334:19

authors 16:13

automatically

142:6 245:4

automating

308:14

automotive 26:4

autonomously

142:6

autonomy 27:4

availability

194:15

available 14:15

14:19 15:3

32:22 35:19

36:21 40:7

75:5,12 90:8

92:6 106:4,13

160:19 161:10

173:8 185:2

186:9,18 187:2

188:18 189:3,4

189:5 205:18

219:5 220:13

250:2 287:1

295:4 306:11

325:17 339:1

339:13

avalanche 34:18

35:13 37:2

ave 1:13

avenues 356:4

average 141:8

152:14

avoid 68:8

208:16 366:4

avoiding 5:6

avoids 12:14

awang 7:15

aware 79:17,18

153:12,17,18

154:14 157:12

173:8 252:20

266:12

awareness 79:15

172:10 174:21

309:19

awful 43:11

96:11

axes 45:3

aye 4:18 384:11

B

b 91:10 198:4

back 8:10 25:20

57:12 76:21

80:17 87:15,17

87:19,22

106:21 134:12

138:5 142:9

156:1 163:12

171:12 181:8

184:12 203:9

217:11 255:13

284:15 286:15

305:12 318:9

318:16 321:3

334:9 341:16

347:13 354:14

356:14 366:6

366:12 373:20

374:11

backdoor 166:1

166:9 187:9,16

300:12 314:20

backdoors

111:18 112:17

backed 270:22

backend 300:4

359:4 372:4

background

36:5 106:22

135:6

backwards 60:1

bad 18:8,15

20:18 24:15

46:22 161:6

174:18 188:10

201:19 263:4

275:1 301:18

310:21 315:4

360:15

bag 307:9

bailiwick 86:10

106:15

balance 8:17

19:20 22:22

33:5 52:4 53:1

53:7,11,13

64:5 73:6,9,10

73:10,12 133:8

133:12 134:1,4

134:6 167:20

169:20 170:3

176:14 178:18

260:4,6 302:21
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

6

319:16 337:4

337:12 345:8

345:12 383:19

balanced 19:18

130:7

balancing 19:15

25:2 52:14

53:22 64:12,14

64:22 65:16,18

65:21 66:13

67:16 68:15

72:9 204:4

291:9

ballot 27:17

ballroom 4:6

baltimore 385:5

ban 123:12

180:12

banning 121:15

bar 29:16

base 167:15

based 9:7 17:18

18:13 44:10

48:19 53:17

65:13 81:21

125:20 147:5

156:11 164:4

176:5 206:15

230:7 237:4

252:3 363:12

366:5

baseline 81:8

218:12

basement

306:16

basic 61:8

136:18 205:22

234:22 256:18

257:14 283:4

342:15,16

basically 72:9

104:22 121:17

138:18 166:11

200:6 273:16

basis 54:6

184:20 209:16

233:13 235:1

240:17 274:18

299:3 307:19

317:8 364:6

bates 373:15

377:11

battle 243:6

bay 113:18

beach 300:11

beaker 347:10

bear 239:8

244:16 314:8

bearing 378:10

bears 89:4

becky 225:11

237:22 249:21

260:2 261:15

262:8 268:9

276:7 282:15

354:19

beckys 255:16

becoming 13:16

138:21,22

139:3 147:18

148:9 170:8

182:15,17

bedoya 3:4

117:3,9 146:22

147:14 162:15

162:20 167:5

178:9,19

183:19 187:20

192:20 196:18

197:8 200:20

201:2

beginning

171:22 205:11

233:21 273:11

274:17 275:14

337:3

begins 60:17

behalf 65:21

behave 37:21

95:20

behavior 16:7

16:10 34:20

35:1,3 68:7,8

68:19 69:3

85:6 280:13

behavioral 36:2

behemoths 44:1

belief 125:4

beliefs 153:4

believe 17:6

32:8 65:22

113:12 115:15

127:13 130:1

131:4 137:17

150:20 151:2

164:2 179:14

184:14 187:1

230:22 235:5

254:11 313:18

317:1 330:9

345:12 368:20

372:20,21

believes 42:6

296:8 297:13

beltway 197:4

benchmark

119:7

bend 256:17

beneficial

274:16 381:18

benefit 50:22

123:5 202:12

286:2 310:18

318:13 330:21

336:14

benefits 50:13

50:21 52:2,5

52:14,18,21

106:22 107:2,3

112:21 277:12

291:16 331:17

benign 152:10

best 12:10,11

38:6 63:15

88:3 105:8

112:12,22

148:2 177:20

197:22 213:7

213:10 251:17

258:16 296:5

309:10 343:8

344:3 373:20

380:18

beta 252:12

beth 88:8 203:8

219:22 278:11

better 25:2

31:10 50:3

53:1 112:14

129:21 143:15

146:6,14 166:5

182:15 187:9

189:11,17,19

191:12 195:19

204:15 208:15

235:3 237:7

258:12,15

264:3 267:5,6

292:16 358:14

373:22

beyond 21:14

39:22 61:20

74:22 82:1

312:8,19 335:9

366:4 367:13

bias 313:1

biases 315:14

bieber 368:3

big 35:4 44:1,22

49:16 50:14

52:2,8,14,21

77:18,20 93:16

114:22 186:5

190:11 228:20

233:22 234:20

237:8 257:4

269:7 288:6

289:13 297:9

317:1,3 319:22

321:22 374:3

bigger 257:5

biggest 92:8

121:8 241:15

267:22

bilaterally

153:14

bill 8:12

billion 141:9

binary 198:19

bind 323:18

324:2 326:2

binding 330:15

331:6

biographies

286:22

biological

227:12

biometry 170:14

bios 10:3

bit 45:6,14

53:21 54:19

57:18 92:18

159:3 162:19

165:19 179:20

190:21 204:7

205:20 213:4

217:9 218:18

233:16,18

246:10 248:10

252:2 262:15

271:7 277:4

288:8 305:14

347:5,14

bits 268:14

black 307:12

blank 334:12

blend 196:6

234:17

blending 233:16

blind 194:3
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

7

361:20

blinking 22:14

block 183:12

blocks 235:10

236:14

blurring 108:5

board 1:3 2:1

4:12,13 6:10

6:10,11 9:18

10:12 11:13

25:11,14 26:14

27:15 28:3

32:5,15 41:2

51:12 56:9,13

74:12 85:20

96:14 98:15

101:6 110:4

116:10 120:21

128:15 140:12

141:17,19

159:15 163:12

163:21 176:3,4

181:9 185:7,7

192:1 207:2,8

207:10,17

209:20 213:18

213:19 216:8

236:20 252:16

282:15 286:10

287:16 295:21

303:20 304:1

357:3 367:17

383:21,22

boarding

294:18

boards 4:4 5:18

7:13 32:20

203:5 207:11

207:18 287:4

349:13

bodes 88:16

bodies 32:21

72:5 94:11

body 33:2 49:1

73:3 74:7

boil 232:8

boiled 347:12

bolton 346:7

bolts 96:1

book 93:16

288:7 306:20

books 93:13

boost 302:1

border 132:18

290:5

boss 23:5 193:15

bottom 50:4

273:16 326:1

bound 57:16

313:9 324:9

345:18

bounds 22:8

42:8

box 192:12,19

277:2,5 320:17

boxes 232:21

bradfordfran...

7:14

brakes 40:8

branch 2:14

25:5 212:22

223:4 227:9

246:20

branches 73:20

315:2 360:16

brand 2:4 4:14

80:14,15 81:22

82:21 83:3

87:6 159:18,19

161:18 162:15

164:14 165:13

192:3 271:5

272:10 275:20

276:12 285:7,9

295:13 303:8

313:3 318:7

322:13 323:21

326:7 328:2

331:18 333:11

335:22 338:10

340:7 341:16

344:10 347:16

363:2 372:8

381:6 383:1

breach 62:6

169:19

breached 300:7

breaches 38:3

breadth 245:9

251:9 278:21

break 7:4 109:2

187:14 195:3

284:18 346:10

brennan 2:13

10:7

brief 7:2 18:6

31:12 50:9

76:2 79:6

102:7 165:17

175:21

briefings 193:12

253:20 374:12

briefly 10:5 53:4

87:8 92:17

200:7 299:15

305:18

briefs 375:3

379:7

bring 88:10

110:9 216:18

231:1 239:8

244:13,14,16

314:8 318:2

327:7 378:6

bringing 165:11

376:17,17

brings 50:20,21

broad 12:2

61:16 152:7

240:22 251:9

262:18 299:22

324:15 383:11

broadening

253:7

broader 33:6

231:2,12

289:17

broadly 19:9

45:17 240:21

broke 16:17

brought 57:15

238:13 341:17

brown 3:13

215:10,17

216:7 239:17

245:22 263:11

267:13 277:16

281:9

brute 61:21

89:21 90:3

195:5

bubbles 245:2

bubbling 263:3

build 35:21

37:22 41:4,11

56:7 61:2 85:8

112:3 125:14

127:12 135:11

135:17 138:11

138:18 140:14

142:1 155:1,7

155:11,12

156:2,7,11

158:10 170:22

174:20,22

199:17 202:7

236:17,22

247:10 271:2

307:18 311:19

346:7 348:3

349:4 356:4

357:13

buildable 135:4

140:21 158:5

171:4

building 86:12

135:3 138:16

140:4 155:8

157:20 158:3

232:16 235:10

236:14 237:4

239:5 251:21

305:1 312:1

347:22 357:5,8

builds 134:16

304:4

built 105:14

126:9,11

136:17 157:18

201:16 214:12

214:13 230:6

242:8 362:12

bulk 15:15

90:17 114:13

128:7,9,9,12

180:2,5,12,18

180:21 200:21

201:3 383:15

bullet 172:7

332:9 370:8

burden 322:18

323:4 330:2

burdens 324:6

burdensome

74:20

bureau 240:9

bureaucracy

351:21,22

352:1 365:9

bureaucrat

22:11

bureaucratic

353:22

burgeoning

78:18

burglars 194:21

195:2

buried 27:7

burn 307:9

business 11:14
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

8

108:13 125:17

126:10 127:2

264:22 265:17

265:22 266:4

266:14

businesses

265:3

button 201:22

349:5

buy 24:7 25:1

buyin 23:20,21

buying 348:16

C

c 1:14 4:8

124:22 248:1

cabinets 151:10

cables 130:18,20

cake 86:15

calibrated

218:15

california

133:14

call 4:16 37:10

48:10 49:1

54:2 85:14

121:18 139:2

152:11 156:14

156:21 175:11

180:18 289:13

289:14 382:9

called 45:13

54:7 120:22

132:15 156:15

198:13 253:10

288:7 317:2

335:20

calls 32:3

163:20 377:4

camera 170:17

cameras 170:15

318:14 369:17

camps 122:17

candor 29:7

cant 17:6 22:22

23:6,16 32:1

32:10 48:20

53:8 59:19

64:4 65:9

70:20 71:4

76:10 77:16

79:15 84:16

86:22 94:8

107:14 116:1

145:4 162:18

163:3 174:17

188:21 198:20

241:17 261:22

262:1 271:10

271:18 272:4,8

278:18 288:19

311:4 326:20

330:9 331:9

349:2,4 367:11

capabilities

170:6 171:7

191:20 309:5

309:15 348:15

357:5

capability

279:14 345:1

capablilities

234:10

capacity 41:5,12

206:10 215:12

capital 3:20

313:5

car 26:2 106:3

321:8

card 6:15 9:12

9:12 17:21

37:5 59:22

66:14 67:10

110:1,2 206:7

286:5,6,17

293:17 354:4

cards 6:15 110:7

229:11 255:3

286:4,17

287:10 318:9

care 8:15 15:20

15:22 17:8

138:15 149:20

150:1 197:1,4

197:5 198:20

199:7 247:1

355:3 356:20

career 247:20

careful 207:19

210:12 248:10

294:1

carefully 88:1

140:11

cares 292:19

carnegie 251:8

carried 136:15

carry 137:10

214:19 259:2

265:6

carrying 383:18

cars 106:1

case 65:9 67:4

76:8 81:17

86:11 95:21

133:14 166:7

168:11 184:14

218:16 259:19

264:7 270:15

281:4 291:22

295:5 326:21

331:10

cases 48:12

53:15 71:19

83:7,12 113:20

114:7 153:10

160:14,22

162:10 171:3

191:4 265:9

270:9 376:10

377:7,14

cast 52:4 123:16

castles 300:10

cat 8:5

catagorization

236:6

catagorize

235:14,16

236:4

catagorizing

268:10

catalogue 27:9

catch 173:15

175:6 311:22

catches 311:21

catching 190:6

cate 3:18 286:21

287:2,6,8

296:14 326:9

328:2,16

335:22 336:2

341:17 350:19

351:12 354:18

355:8 374:4,5

375:17 381:9

381:12

categories 152:2

306:2

cates 305:22

cato 66:16

cats 368:3

caught 310:20

cause 20:15,20

29:17 46:14,15

46:22 47:1

53:17 65:3,14

73:1 81:21

104:18 169:5

362:3

caused 50:6

104:15

causes 16:6

20:19 47:17

237:3 359:13

causing 17:3

cdt 297:13

299:11 303:1

375:3

cell 194:12

cellphones

329:15

census 122:9,15

123:10,11

center 2:13 3:4

3:19 33:13

40:3 62:2

117:4 132:13

156:3 232:5

295:16 296:7

centers 10:7

130:18,21

186:1 188:4

centralized

127:1

centric 234:3

century 337:17

ceo 357:12,14

certain 13:7

16:15 37:5,6

42:1 46:14

52:12 63:4

68:9,9 71:5

73:17 76:15

83:12 84:4

85:18 102:21

103:15 106:6

159:5 160:14

165:8 173:3

174:3 184:13

190:2 220:10

224:4 235:21

268:10 279:7

380:22

certainly 17:9

17:15 43:2

45:2,4 58:5

59:9 60:7 64:3

70:21 84:17

94:14 98:16

108:14 146:13

149:5,15,17
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

9

153:19 154:9

157:15 158:7

168:3,12

178:19 179:16

180:22 184:7

188:2 192:20

197:11 198:5

224:7 230:12

243:17,22

254:20 276:2

282:5 288:15

289:9 293:15

296:13,15

304:21 317:20

333:7 341:5

355:17 356:14

377:13

certainty 159:13

certification

376:6 385:1

certify 385:6,9

chain 189:3

266:11 308:15

349:6,7

chair 74:13

108:17 110:13

chairman 2:3

4:11 25:10

27:10 101:21

109:8 165:14

201:8 383:2

challange 253:2

challenge 83:16

84:21 92:9

200:18 221:6

222:10 241:20

241:22 264:20

265:11 267:22

328:19,21

335:18 353:9

374:18 379:9

380:1

challenged

132:11 329:11

challenges 26:1

226:19 241:16

279:12,17,22

327:19 378:20

challenging

204:6 226:17

championed

50:16

chance 13:12,13

333:16

change 16:7

123:9 137:20

145:18,21

146:1 193:1

280:13 288:6

315:21,22

322:11 341:8

341:11 361:3

381:1,1

changed 16:10

17:15 70:13

137:5,6,8

149:15 190:4,5

227:22 228:1

228:10 229:5

334:5 341:11

changes 137:13

147:8,11 215:3

226:14 278:1

364:7

changing 33:19

110:21 277:18

348:20

channels 223:16

228:15 280:12

chaplains

122:21

chapter 288:7

characteristics

46:10

characterize

209:8

charged 360:21

chatty 139:4

cheaper 138:13

148:10 169:13

185:16,17

check 59:5

277:2,5 325:3

checked 277:14

checking 192:12

192:19 232:21

checkpoints

126:13 172:22

173:3

checks 173:10

175:2 320:17

chemical 227:12

chertoff 25:6

cheshire 8:5

chew 383:16

chicago 183:9

183:10

chief 3:7 117:6

124:11 133:13

134:15 215:10

223:17 240:4

childhood 13:8

13:13

children 303:22

childrens 341:5

chill 21:3,4,5

chilling 21:9

79:2

china 170:18

chinese 189:13

choice 23:13,16

23:17,18 66:1

66:2,3,4 78:2

126:3 255:9

256:22 289:11

299:9 307:21

choices 106:10

199:4 270:3

choose 297:2

299:5 319:7

326:20

chooses 77:22

77:22

chosen 13:9

chris 3:20 313:4

348:1 351:6,15

358:1,13

360:11 364:1

church 73:22

210:9

circling 262:14

circulate 9:22

circumstance

12:5

circumstances

13:6 64:20

80:1 84:17

99:14 101:8

circumvent

175:5

cited 375:3

citizen 140:10

201:21 337:18

337:20

citizenry 314:8

337:6

citizens 132:9

133:10 167:17

302:20

citizenship

230:21 232:5

civial 215:14

civil 1:3 4:3

111:4 116:9

135:15 203:5

205:10 206:5

206:11 207:16

209:2,13,19

211:3 215:11

215:18,20

216:1 217:16

222:21 223:17

223:19 225:12

225:16,18

226:6,9 227:2

227:18 229:4

229:14 231:1,3

231:12,14,18

231:22 232:4

232:17,19

233:4,10 234:5

234:16 237:6,7

237:12 238:10

238:12 240:3

242:15,21

243:8,19

244:10,13,18

275:7 280:22

282:21 284:13

289:19 295:17

295:21 296:2

303:9 304:18

305:5 310:21

317:10,12

334:1 349:13

351:18 353:8

356:21 357:6

358:21 372:17

383:19

civilizations

8:11

claims 9:7

clarify 180:17

clash 20:22

class 136:3

classic 329:4

classified

253:19 353:4

356:8

clause 64:14

clear 90:20

123:8,15 128:8

131:12 133:16

149:22 158:9

158:10 191:5

202:2 230:11

295:2 331:15

337:21 370:9

375:10 378:8

381:12 382:19
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

10

clearance 193:9

201:7 355:2

clearances

193:6

cleared 85:21

253:3,13,16

254:2,9,11,14

254:18,21

clearer 146:14

291:3

clearing 254:5

clearly 80:10

119:15 163:2

219:2 223:12

249:9 331:16

332:17 336:7

375:6

clie 136:9

client 29:5,8

354:10

clients 267:18

268:2

close 13:8

140:22 141:14

closely 88:4

133:2 207:11

closer 323:22

closing 115:14

cloud 125:8

136:22 151:11

151:14,17

188:19

code 187:5,10

coded 32:6

coding 234:13

codirector 10:7

coerce 104:1

coercive 44:7

58:22

coffee 77:3

coffin 150:12

cogent 41:19,20

cognizable

98:14

cold 10:10 228:2

collaboratively

246:21

colleagues

148:20 216:21

collect 9:22 34:2

40:15 61:22

62:4,11,20

63:4 70:2

79:22 87:7

106:9 118:1,14

118:15 120:9

121:14 178:21

179:1,3,10

277:19 292:22

293:4 298:3

324:1 342:2

352:22 353:1

359:21 360:7

382:12,14

collected 5:11

15:12 19:8

21:15 37:11

63:7 79:18

84:3 90:14

99:19,19 118:9

121:16 123:3

126:2 127:16

152:8 163:16

164:5 169:11

202:16 204:17

221:19,20

229:22 230:20

232:11 266:13

278:20 282:20

306:9 324:5

330:11 358:2,5

366:20 371:6

collecting 36:7

36:20 39:4,10

40:1 55:17

62:12 75:15

77:19 118:13

142:5 165:7

200:2 208:15

281:18 283:9

301:14 339:15

350:16 353:18

collection 15:15

15:15 20:13,17

30:20,22 34:5

36:11 37:18

38:13,17,19,20

38:22 39:2

40:19 46:4

56:2 61:15

63:10 66:9

69:8 77:14,17

77:20 78:13,22

82:6 84:7,16

87:12 89:12,22

90:4,9,11

92:21 102:13

102:14,15

104:17 108:4,4

108:7 118:7,20

118:21 120:4

120:20 124:4,7

126:3 128:7,10

128:12 136:6

147:19 169:12

169:12,21

178:4,4,10,14

178:14,14,17

179:7 180:5,12

185:18 200:21

201:3 228:3

230:5 232:7

256:1,3,5,6

257:21 258:4,6

258:21 260:14

260:19 262:18

262:20 271:14

272:6,16

278:22 279:4

279:20 280:5,9

283:7 292:12

292:13 293:8

296:17,20

297:7,14,15,21

299:1,7,8,11

299:21 300:8

300:15,22

301:7,8,16

302:18 303:6

305:21 317:2,4

323:2 324:11

325:5 327:15

333:9 338:3

350:11,12,17

350:21,22

351:3,8 359:17

360:3,6 361:14

371:9 372:6,10

372:20 383:12

collections

180:2

collectively 11:4

collectors 237:1

collects 15:21

16:3 298:11

300:8 317:19

college 137:2

157:9,10

collins 2:7 4:14

88:14 92:8

171:10 173:17

175:8,18 196:8

203:9 206:9

207:3 215:9

225:8 237:20

239:10 241:4

241:14 245:7

247:11 249:21

252:14 255:1

255:10 262:5,7

263:9 271:4

278:2,13

280:20 282:14

284:14 363:3

366:8 370:10

colorblind

287:9

com 93:13

comb 322:2

combat 313:21

combination

268:15

combinations

268:20 269:1

combinatorial

138:3

combine 36:15

47:7 75:13

76:12 335:1

combined 41:8

217:22

combing 14:16

combining

76:13

come 16:22

29:11 57:15,17

77:6 104:12

124:18 132:16

134:12 140:20

142:9 180:9,11

180:14 216:12

217:16 228:7

229:5 257:14

259:21 271:22

272:21 289:10

305:6 319:11

322:19 324:7

325:21 347:3

367:21 372:18

373:5,12,18

comes 10:11

12:9 21:9 25:2

40:13 53:6

63:13 123:19

133:14 149:11

221:2 223:15

223:16 245:3

249:16 296:6

297:20 323:20

334:9 345:17
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

11

358:9 361:4

comfort 253:9

comfortable

24:16,19

comforted

177:22

coming 17:21

88:12 171:6

216:6 243:16

273:2 339:19

341:1 358:12

commencing

1:14

commend

271:13

comment 50:9

185:14

commentators

8:9

commented

194:8,19

comments 5:17

7:9 67:18 76:2

128:17 134:10

183:18 186:13

225:7 263:14

313:15 317:15

331:20 383:22

384:2,4,5

commerce

247:22

commercial

39:7,8 43:12

75:7,9 108:3

111:19 119:4

216:16 230:17

299:6 304:3

343:6,6 350:14

commercializ...

104:10

commercially

39:17

commerical

228:17

commission

85:13 216:12

216:15 385:18

commissions

287:5

commitment

116:10 125:6

131:1

commitments

126:6

committed

282:7

committee

30:11 73:22

119:12 163:1

179:17 193:22

210:9 303:12

committees

85:13 179:14

194:2 210:7

212:20 253:16

253:21 254:6,9

254:16

common 18:8

30:3,6 46:9,10

115:4 224:15

commonality

96:12,18

commonly

292:19 293:10

commonsense

14:21

communicating

153:1

communication

189:3 228:14

339:11 361:18

communicatio...

29:6 43:16

103:14 128:14

166:3 227:7

228:12,16,18

249:2 363:16

370:22

communities

121:12 123:20

communitity

214:12 242:18

community 30:4

42:6 90:18

92:2 118:11

146:7 195:4

207:14,15

208:8 213:1

214:19 215:5

226:9,10,13

242:11 244:2

245:1 254:4

258:3 266:15

266:20 276:11

310:22 376:1

companies 24:2

44:6,8,21,22

45:2 83:7

90:15 91:3,5

104:1,4 112:4

112:17 127:20

129:9 131:3,6

140:1 149:10

188:15 298:14

298:21 341:22

343:14

company 91:19

102:21,22

103:2,16

126:18 134:16

136:5,12

156:15 172:18

250:15 304:17

338:22 350:15

356:14 367:2

comparatively

58:18,21

compare 264:9

compared 58:21

comparison

11:17 67:1

compartment...

140:5

compartmented

253:17

compatibility

222:17

compel 375:9

compelled 99:15

99:16

compensate

100:16 327:13

competing 8:17

9:2

competition

67:12

competitors

130:22

compiled 148:8

complain 54:4

complete 86:2

249:13 384:1

completely

31:13 86:6

165:1 174:17

330:7 337:9

350:20

complex 38:9,11

74:19 94:12

143:1 171:8

308:19 368:5

378:20

complexity

37:19 38:4

233:22 245:12

373:17

compliance 38:3

38:5,8,11 95:2

95:3,16 127:10

143:2 146:11

169:15 172:14

176:20 177:4

192:16 206:19

216:4 226:2

230:6,7,11

238:4,8,18

239:13,20

240:7 241:2

242:21 246:9

246:12 316:14

324:21 335:13

342:13 343:7

345:22 346:1,2

compliant 146:3

complicated

37:16 63:14

171:20 378:2

complies 177:14

comply 38:19

145:10 178:2

240:14 319:15

342:4

complying

177:10

compoments

224:15

component

22:20 48:16

76:4 83:20

215:20 224:8,8

224:9,10

239:21 246:11

346:12

components

215:20 224:9

224:16 246:8

346:13

comprehensive

10:18 127:5

186:12

comprehensiv...

31:2

compromise

14:10

compromised

15:8

computer 33:12

33:19 61:11

74:21 110:15

134:17,18
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

12

174:10 237:13

295:18 313:10

computers

22:14 269:17

computing

110:13 234:11

comstat 185:1

conceivable

256:14

concentrate

45:20 200:10

concept 8:6,13

12:2,11,11,14

13:3 14:21

17:5 49:22

76:20 80:19,22

82:9 97:19

120:22 121:4

138:19 144:15

147:5 155:10

196:10 235:11

366:3

conception

18:13,14 72:2

340:5

conceptions

25:19 48:1

concepts 61:17

92:9 171:15

conceptualizing

45:13

concern 61:18

127:15 132:3

135:3 147:7

188:14 258:12

concerned 45:8

56:13 149:9

188:7,9,11

concerns 107:5

127:18 171:15

205:10 230:12

230:13 374:9

concluded 45:19

181:18

concludes

202:21,22

conclusion

224:1

conclusions

280:10 288:1

347:3 361:5

concrete 92:13

170:9 312:7

concretely 93:4

163:6

concur 293:15

concurrences

76:7

conditioned

364:15

conditions 9:6

362:14

conduct 31:15

73:6 148:10

179:15 194:4

197:3,12

198:17 211:12

233:4 241:17

363:14

conducted 70:11

193:12 210:2

228:13 230:3

236:19 265:12

332:6

conducting

167:1 251:3

321:18 342:21

conducts 218:19

223:4 225:17

227:22

conference

154:3

conferences

247:9

confessional

27:19

confidence 57:2

57:5 130:5

193:7

confidences

13:18

confidential

122:12

confidentiality

29:5 122:14

confidentially

123:3

configure 308:4

configuring

112:5

confinement

59:6

conflict 67:15

confluence

108:15

confusing 96:16

conglomerate

8:14

congratulations

296:4

congress 11:13

12:7 32:22

65:20,21 73:22

74:4 85:16

95:4 122:14

123:12 128:19

130:14 210:4

212:6 230:15

253:5,6,11

254:1,14,16

352:14 378:17

congressional

179:14 210:7

212:20 252:18

253:2 303:18

321:3

connect 39:13

connected

138:21,22

169:14 371:15

371:19

connecting 49:3

conscious 357:6

consent 79:11

79:12 80:11

81:3,18 98:7,8

152:16 221:8

288:20 312:14

328:6,11 329:5

331:11 332:13

consenting

103:7 328:9

332:19

consents 289:1

consequence

55:9 56:3 60:8

consequences

20:11 36:7

60:19 62:4

281:1 334:7

consequential

54:2

consider 5:22

113:16 114:9

177:16 227:18

240:9 247:5

284:1 344:22

383:17

consideration

114:21 143:13

154:19 176:22

205:11 250:6

253:7 296:15

considerations

111:7 199:21

231:16 285:6

303:4 345:4

379:2

considered

115:7

considering

231:22 245:9

considers 230:4

364:13

consistent 209:3

247:6 299:20

consistentily

230:3

consistently

95:20 123:18

consists 8:19

constantly 54:21

constituents

153:22

constitues 235:4

constituted

323:2

constitutes

319:18

constitution

42:1 131:4

244:5 313:19

314:13

constitutional

42:9 98:13

129:12 147:4

337:3 377:8

constitutional...

97:21

constrained

314:9 334:17

375:20

constraint 325:3

constraints

324:6 325:16

345:17,17,19

constructs

324:17

consult 176:11

176:18

consulted 242:4

consulting 2:14

25:5 242:15

consumer 366:6

consumers

125:9 298:22

consumes 208:1

contain 34:16

34:22 35:2

contained 385:6
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

13

contains 371:14

contemplated

149:18

content 132:13

151:22 152:19

153:2 168:6

188:22 230:19

343:18

contents 36:10

290:9

context 3:2 6:1,8

14:6,7 60:6

75:17 81:1,12

82:13 83:11

84:13 85:2

88:21 93:10,20

93:22 94:2

95:9,10 109:12

114:20 119:15

132:11 133:21

135:13 144:18

160:4,20

161:15,22

162:14,17

163:11,21

164:7 168:4

169:1,10

181:21 192:7

195:2 204:3

205:3 208:3,4

217:3 218:14

218:19 219:4

221:7 276:21

290:16 297:11

305:19 327:1,3

327:11 328:15

332:18 340:20

350:15

contexts 32:18

134:2 160:5

304:9 309:13

continually

24:20 134:3

continue 124:8

131:12 134:7

181:10 188:16

198:21 203:6

211:14 234:3

255:15 280:15

294:12 355:22

364:10

continued

130:10

continuous

345:22 346:2

continuously

346:8

contract 102:22

103:1,5 198:10

contractors

104:8

contractual

103:12

contrary 151:5

224:3

contrast 31:3

35:22 59:8

113:12

contribute

41:21 234:4

contributes

362:18

control 5:10

12:13,22 14:1

15:8 16:4,5

17:16,20 28:8

28:8 31:15

37:21 42:20,20

64:1,7 67:22

68:2 69:7,20

71:5,7,9,11

78:10 80:17,20

83:21 87:1,16

106:5 121:2

156:3,4 233:8

293:11 302:15

307:17 308:11

340:11,12,13

340:18 342:1

342:18 349:20

controlling 13:3

13:4 86:5

142:15 143:20

controls 12:20

38:13 71:13

93:2 94:8

199:2 209:17

298:17 306:4

307:18 308:2,5

310:14 348:2,4

348:9,10

353:21,22

376:3

controversial

65:6

controversy

332:13

convenient

113:3

converged

335:20

conversation

135:6 262:13

277:13 282:13

362:1 365:5

370:3

conversations

166:11 274:6

275:10 283:18

convey 34:19

36:9

conviction 184:4

convinced 11:12

cook 2:7 4:14

88:13,14 92:8

171:10 173:17

175:8,18 196:8

203:8,9 206:9

207:3 215:9

225:8 237:20

239:10 241:4

241:14 245:7

247:11 249:21

252:14 255:1

255:10 262:5,7

263:9 271:4

278:2,13

280:20 282:14

284:14 363:2,3

366:8 370:10

cooks 88:8

cooperation

104:20

coordination

91:5

copied 290:9

copies 361:20,20

core 29:2 118:22

120:22 124:9

125:4 234:19

304:11

corner 183:22

183:22 184:1,3

184:4

corporate 79:9

79:17 108:13

144:15

corporation

107:1,4

correct 48:14

87:1 181:5

272:9

corrected

194:13

correction 31:6

55:10 56:7

57:6,13

corrections

374:13

correctly 45:20

73:11 166:20

199:11

correctness 87:3

correspond

34:15

cost 50:22 115:9

115:11 133:15

141:9 217:16

310:18

costs 68:13,21

342:20

couldnt 81:6,17

104:7 201:1

counsel 96:4

117:6 124:12

209:11 238:16

239:12 243:15

243:16 244:7

295:15 385:10

counsels 238:3,7

240:1

count 279:14,16

279:20

countermeasu...

69:3

counterterrism

205:3

counterterror...

6:1,8 109:12

111:4,14

114:16 119:9

144:19 162:14

165:20 166:4

204:2,13,14

205:7 208:5

213:21 216:19

217:6,10

218:14 219:18

227:10 246:4

323:11 325:15

countertrroris...

3:2

country 10:22

44:12,22 187:5

187:18 284:11

301:17

countrys 110:15

counts 361:12

county 385:5

couple 102:2
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

14

138:8,9 175:20

199:12 224:2

237:19 249:22

273:12 309:18

319:6 340:22

361:12 381:6

course 5:15

51:15,19 76:18

90:14 99:8

110:8 119:14

136:14 150:17

173:13 207:22

209:18 210:4

214:14 216:14

217:12 218:13

219:9 222:10

222:16 223:16

239:19 249:4

253:17 257:8

258:8,20

260:11,15

264:15 266:4

267:8 281:15

293:18,19

299:5 304:12

309:9 347:6,6

347:14 367:6,8

375:8

courses 311:17

court 12:1,4

14:12,16,18

54:5 65:5,8,13

71:17 72:1

74:22 76:8

129:8 130:4

147:16 150:14

151:12 196:13

212:11,21

221:14 245:18

292:9,14

301:21 314:22

325:18,19

358:18,22

365:11 369:9

373:7,12 374:2

374:13,17

375:6,20 376:1

376:4,10,19,19

376:21 377:3

378:5,14,17

379:5,14 380:4

380:11,18

courtappointed

374:22

courtesy 361:20

361:20

courts 8:20

11:13,17 65:18

66:18 97:6

130:11 132:12

133:15 364:17

368:17 372:13

372:16 374:6

375:2,8,17

cover 172:13

224:13

covered 14:13

105:15 349:10

383:11

covers 97:2

101:20

cpo 177:8

cracking 187:10

craft 87:21

187:17

cramped 19:11

crazy 354:16

create 34:18

47:11 75:13

76:15 104:19

105:3 117:21

126:8 132:16

140:5 158:17

212:9 258:9

284:4 349:22

353:3 356:22

created 151:15

152:8 209:12

209:18

creates 49:11

50:17 175:7

305:1 353:12

creating 72:17

135:4 140:17

158:3 183:20

244:10 276:18

303:19 313:1

creation 206:20

360:17 369:3

credit 336:12,13

creep 366:4

crime 133:3

183:21 184:1

184:17,17,18

184:20 295:18

371:15

crimes 183:16

183:16 184:3

criminal 53:17

65:3 182:12

183:5,13 217:3

221:9 222:10

222:22 372:14

criminals

110:22 111:17

113:1 182:9

critical 11:1

31:16 55:10

89:11 111:14

113:7,21

114:15 116:12

119:7 125:7

132:22 187:6

207:15 224:22

232:19 281:5

293:11,14,19

300:6 351:9

352:15 378:13

379:4

critically 207:20

criticized 112:9

183:11,17

critize 273:4

cross 132:18

351:1

crossed 172:21

crucial 114:22

372:7 375:18

crumple 40:9

crumpling

307:10

cryptographer

134:18

cryptography

40:15 91:17

138:2

crystallize 202:5

crystallizing

148:14

cues 297:19

culture 95:1

172:1 215:3

251:19

current 44:19

58:14 101:7

189:16 217:13

231:1 369:14

currently 70:5

190:1 212:2

283:8 313:4

customary 51:9

customer 125:7

125:19 127:7

132:12 150:5

172:18 188:6

188:14 265:19

265:21,22

266:9,11,14

352:18 353:2

customers 125:5

125:9,15

127:13 131:21

149:8,22 150:7

150:21 167:21

168:1,2 188:3

188:7 265:4,5

342:12 367:20

cuts 147:15

cyber 111:15,22

133:3 313:11

cyberattacks

227:13

cybersecurities

349:22

cybersecurity

295:19 343:11

346:5 378:18

cycle 126:13

D

d 1:14 4:8

110:14 124:22

145:19 169:3

248:1 284:6

daily 34:6

374:14

dam 351:2

damage 115:8

damaged 128:6

damages 222:22

damaging 161:1

dan 2:15 28:4

31:22 50:12

65:22 71:2

98:2,17 103:21

dangerous

184:5 210:20

daniel 18:2

dans 77:9

dark 185:11

189:21,22

darlings 136:13

data 18:19,20,21

19:1,2,3,4

20:13,17,20

21:14,16 24:3

33:20,22 34:2

34:2,3,14,14

35:5,12,15

36:2,7,15,22
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

15

36:22 37:14

38:3 39:7,20

40:2,2,18,19

40:20 43:17

46:3 47:3,5,9,9

49:1,4,10,10

49:13,15 50:15

50:16,17 52:2

52:8,14 55:17

61:22 62:2,3

62:10,13,13,14

62:21 63:4,7

63:10 68:12

70:19 76:12,14

77:2,15,20

79:13 82:6

87:1,3 90:14

91:6,11 94:4

102:11 104:17

105:1,1,2,13

113:8,10 114:6

114:22 117:15

117:16,18,19

118:7,8,21

119:1 120:21

121:15,19,22

122:1,11,16

123:3,11,19

124:1,6 125:12

126:1,2,3,4

127:8,14,15,19

128:7,10,12

129:17 130:6

130:18,21

131:2 132:5,13

132:18 139:10

139:16,18

140:6,6 141:10

147:20 149:17

150:8 151:14

151:14,15,20

152:1,8,17

153:5 154:3

156:2 160:15

162:4 164:6,10

164:10 165:8

168:8,11,15

169:7 178:21

179:7,11 180:2

180:8,12,19,21

181:16,21

182:5,16 183:1

185:18,22

186:1,5,5

187:2 188:4,8

188:9,12,18

189:3 190:14

190:19,20

200:22 201:3

228:20 229:21

230:19 232:8

232:11 233:22

234:20 235:21

235:22 237:1,2

237:8 256:8,10

256:13 258:9

258:14,16,18

259:1 262:17

262:17 269:7,8

269:16 270:3

272:12 275:4,4

277:20,20

282:19 289:13

289:14,15

290:16 291:10

292:2,7,9,22

293:4,9,10

294:13 296:16

296:20 297:6,7

297:9,15,15,20

298:3,11 299:7

299:8,11,21

300:3,15,17,19

300:20,22

301:7,7,18

302:5,10 303:6

304:4,5,5

305:19,20,21

305:22 306:3,8

307:15,18,19

307:19 308:6,7

308:7,11,12,15

308:22 310:4,7

310:14,14,16

311:5 312:20

317:1,3,5

319:22 320:4,6

321:22,22

322:5,7,8,8,9

322:10,11,12

323:2,3,3,6,7,9

323:12,13,16

323:19 324:2,3

324:5,10,14,19

325:4,17,22

326:3 327:14

328:1 329:8,11

330:12,16,17

330:18 331:2

333:9 335:1,4

339:4,6 342:12

342:12,22

343:11 348:22

349:17 350:2

350:16 352:22

353:19 359:16

360:3,6 362:13

362:15 366:13

366:16 367:3

367:18,19

368:10,10,10

368:14 370:13

371:5,7,8,10

371:13,18

372:3 381:21

382:12,14

383:15

database 60:14

79:17 160:17

195:2,5 248:20

248:22 365:11

databases 59:17

61:2,6 75:4,8,9

77:17 79:9

102:11 161:3,5

datacentric

232:9

datadriven

182:22

datafied 312:17

datas 52:21

date 37:6 237:16

dave 250:19

david 2:3 10:18

263:9 285:9

day 88:17 97:21

101:22 133:1

150:6 271:7

285:2,12 289:4

290:21 303:21

304:17 315:5

319:16 338:5

383:5,15

385:13

days 57:4

170:16 248:15

312:17 384:7

de 53:6

dead 224:4

deal 93:17

100:13 105:15

133:22 135:15

155:15,15

169:1 191:1,4

191:9,18 239:7

270:8 306:2

325:16 343:14

dealing 88:19

137:9 150:5,6

155:19 171:20

247:17 305:20

310:15 349:16

349:20 374:21

dealings 103:1

dealt 72:18

177:7

deanomoized

269:16

dear 226:21

death 288:7

310:17

debate 96:14

129:3 180:19

226:11 256:4

290:14 310:18

334:14 337:4

376:17 378:21

debated 154:5

212:2

debates 59:14

115:5 131:16

debating 113:5

decade 242:7

decades 123:20

124:21 185:2

340:22 369:13

december

385:18

decentralized

228:9

decide 51:13

100:14 187:13

269:20 376:22

decided 8:21

78:19 273:1

decision 74:17

133:15 145:22

221:13 245:3

257:13,14

261:17 379:21

382:4

decisionmakers

253:12

decisionmaking

116:8 349:6

decisions 5:1

48:18 49:4,5,6

57:20 115:5

140:13 145:7

151:12 242:13
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

16

252:5 309:22

340:19 348:21

350:2

declaration 51:6

declaring 51:14

declassification

301:21

declassified

129:6 377:15

decrypt 112:6

dedicate 319:9

dedicated

119:13 126:21

193:22

deemed 57:7

deeper 172:14

198:22 199:4

deeply 40:22

95:22 247:2

default 112:4,13

115:16 166:14

185:20 187:4

359:8

defend 244:5

defense 122:6

123:16

define 7:22

138:4 140:5

155:5 171:6

208:2 269:10

269:14

defined 45:17

45:18 219:2

223:12

defines 42:1

defining 1:5

2:10 4:4 5:21

51:15 202:3

203:6 204:3

definitely

104:11

definition 11:15

11:19 157:16

208:18 301:4

339:19 368:12

definitions 5:13

11:9,11

definitive 100:3

287:11

degree 59:7

103:13 119:22

254:1 345:5

347:10

degrees 8:16

196:11

deidentification

111:10 113:5

114:4,11,13

115:17 160:3,8

175:16 194:7

195:9,18 196:9

383:13

deidentified

194:10,12

196:16

deidentify

195:14

delegated 85:15

253:10

deleted 307:8

358:6

deletion 307:9

307:12

deliberately

68:9

delineated 53:16

65:12

delinked 196:12

delivering

265:18

demand 24:8

188:7,14

demanding 24:9

demands 129:13

189:6

democracies

32:10

democracy 3:19

27:16 33:4

66:6 253:13

295:16 296:7

democratic

54:13 55:3

56:1,6 302:22

demonstrate

266:16

demonstrating

284:11,12

dempsey 2:6

4:14 96:7,8

97:11 98:19

99:16,20 101:9

102:1 109:6,8

117:2 124:10

134:8,11

141:16 144:6

144:12 145:12

145:15 146:12

146:17,21

149:5 150:11

151:21 153:7

154:11 158:8

158:22 159:14

164:20 165:14

181:8 183:7

184:22 185:4

187:21 189:7

192:1 201:8,11

202:21 278:4

347:17,19

350:6 351:5

354:2

denial 112:14

denied 60:4

221:12

denominator

46:9

deny 59:17

77:10,10

294:18

denying 336:13

department

3:13 25:8

100:5 122:5,6

122:15 123:16

183:10 209:15

213:1 215:11

215:19 216:2

217:4,18 222:7

225:5 226:2

246:3,7 247:1

290:4 303:13

313:11 364:15

departments

215:15,18

216:20 217:1

219:19

departmentwi...

216:3 239:19

depend 269:6

371:22 372:1

dependent

237:9 297:1

332:16 377:9

depending

288:12 360:5

depends 58:10

102:19 162:9

359:18,19

deploy 78:21

deployed 127:4

183:10 304:14

309:12

deployments

304:8

depot 141:11

depths 237:16

deputy 3:20

25:7 215:16

313:6

derive 320:12

derived 227:6

derives 26:22

345:14

describe 227:2

described

342:15 351:21

deserve 42:6

deserves 14:22

362:10 365:2

deserving 43:6

design 61:16

125:17 126:17

135:17 142:12

142:13 143:11

143:17 144:8

144:15 146:15

154:18,20,22

155:16,17,17

158:19 190:2

designed 209:21

211:19 220:17

232:13 233:9

365:22

designs 134:16

desirability

214:8 294:7

desirable 374:15

desire 4:22

254:20

desired 38:10

desk 306:14,15

despite 14:14

38:6 234:21

destroy 167:2

destroyed 222:1

300:19

destruction

118:8 365:17

detached 374:17

380:4

detail 175:12

218:18 220:22

237:15

detailed 34:22

75:14 122:15

129:12 130:1

308:17 309:2

details 151:16

detain 298:11
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

17

detect 339:3

detection 208:16

deter 192:15

determination

258:22 300:20

358:7 359:6,8

359:12 377:20

determinations

258:3

determine

114:10 235:6

235:15 236:3

276:3

determined

34:14 113:18

determining

40:19

deterrent 325:8

325:13

detrimental

161:6 166:3

develop 39:3

114:18 126:18

148:6 173:2

235:6,10 236:6

236:8

developable

92:6

developed

114:20 117:14

127:4 148:8

173:1 177:12

234:13

developing

54:17 91:22

92:10 153:14

154:8 234:8

263:19 276:9

development

5:3 126:12

developments

129:5

develops 225:19

device 136:15

155:20 156:4,7

156:20 185:21

290:8

devices 39:16

136:2 137:9

138:5,14,16,18

138:19 139:4,8

142:5 143:12

155:11 158:4

185:16 188:5

290:6 329:16

devolve 277:14

dhs 205:17

250:13 271:16

272:3 273:5,17

276:2 282:9

326:17

dhss 273:4

dialogue 154:6

313:14 365:8

dias 216:21

didnt 13:14

81:18 136:21

147:1 167:10

185:10 201:6

243:22 271:2

326:14 347:13

352:11

differ 93:20

248:18,19

376:11

difference 43:19

44:3,3,5,5

52:19 73:16

199:12 327:22

differences

264:13,16

299:14,19

different 6:10

10:16,17 11:9

11:21,22 12:3

18:18 21:5

27:8 28:16

32:18 33:15

39:12,12 47:4

48:3 63:18

68:10 79:3

89:17 94:10,11

96:17 97:2,3

103:11 157:16

161:4,5 165:1

172:17 173:10

177:9 184:9

202:13 212:15

216:15 230:13

232:10 235:16

235:19 236:4

236:12 238:6

241:5 244:19

244:20 248:13

249:3,5 250:17

251:1,7 252:2

259:19 260:14

262:11,13,15

263:7 265:14

273:8,12

277:20,20

297:22 298:9

304:20 309:13

314:7 315:22

316:20 319:4

319:19 323:15

323:15 327:21

334:6 339:21

359:9,13

373:12 381:2

differentiating

299:2

differently 21:7

21:8,8 259:18

differs 93:19

difficult 36:8

37:20 38:12

86:18 138:5

143:2 162:2

163:7 169:16

169:22 170:1

214:10 233:21

235:11 238:20

269:3 287:17

290:10 330:4

368:17

difficulty

374:19

diffusion 186:11

186:12

diginity 231:8

digital 49:2

136:10 234:10

diligent 14:15

207:18

diligently 246:8

diminish 52:18

147:9

diminishing

192:13 319:12

diminishment

52:19

dinner 161:14

diog 217:21

220:4,8 221:17

221:18

direct 102:5,6

115:9 280:21

299:7 318:9,19

326:10 378:10

directed 102:10

228:4 271:12

381:10

direction 203:16

328:21 331:22

369:11

directives 243:1

directly 30:17

34:6 43:10

206:12 227:14

276:1 300:15

307:3

director 3:11,20

112:9 117:4

206:6,12

225:15 226:1

250:18 252:10

295:15 313:6

directories

194:15

disagree 89:16

296:13

disagreement

25:18 26:13

disappear

156:17

disappearing

140:8

discard 101:6

discern 190:22

discharge 123:4

disciplines

237:10

disclose 34:6

99:3 102:20

129:12 266:5

280:18

disclosed 79:11

209:22 222:19

discloses 150:16

disclosing 34:8

91:18 142:6

disclosure 5:7

13:19 81:19

84:9 97:16,17

97:19,20 98:3

98:8,8,21 99:5

99:6 222:16,21

disclosures 16:9

16:11,14 57:10

120:6 163:14

222:11 278:21

discontinue

32:6

discount 69:14

discourse 17:3

discoverable

229:2

discplinary

281:21
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

18

discreditable

18:16

discreet 89:6

discrepancies

269:9

discrete 335:1,4

346:1

discrimination

123:7,21

discuss 9:5 10:1

36:6 63:11

105:11 205:2

237:15 280:15

discussed 79:1

119:2 154:4

204:7 210:1

212:2 226:16

234:1 358:18

discussing 220:8

290:16

discussion 5:15

7:3 10:13

25:14 27:13

29:7 63:14

119:11 124:17

124:18 145:6

155:4 197:13

213:4,6,11

215:7 234:4

235:13 268:12

297:5 353:20

383:8

discussions

131:22 153:20

165:11 235:19

338:20

dishonorably

123:4

disincentive

292:12

dismissed

353:14

disparate 96:17

121:11

displayed 43:20

disposition

222:3

dispute 58:6,7

disruption

46:14

disruptions

45:21

disrupts 47:18

dissemination

323:3 324:12

325:6 351:10

dissimanation

258:5

dissonance

108:8

distinction

266:3 336:6

distinguish

380:9

distrust 349:8

distrusting

155:21

disturbed 5:5

diverse 33:4

dividend 190:7

division 216:13

219:19 223:3

246:19 250:16

doable 140:17

doc 99:13

doctor 98:22

99:5,9,11,14

332:20

doctors 99:4

doctrine 13:21

72:5 97:13,13

97:22 151:6

168:17 197:13

353:15

document 272:4

324:17

documentation

249:10

documented

331:16 354:1

382:4

documenting

233:7

documents

218:1,10 220:3

doesnt 13:9

15:21,22 20:1

20:6 23:9 36:9

42:21 46:21

50:2 52:7 59:4

65:11 69:12

71:22 72:11

80:12 82:9

93:17 97:22

100:15 149:20

151:6 163:15

167:14 192:15

197:14 218:22

248:3 258:17

266:11 271:21

272:14 282:1

294:6 315:4

323:12 328:7

356:1 362:20

364:6 381:1

doing 22:12

23:7 52:9 68:8

91:7 95:17

116:3 136:1

139:15 174:10

177:15,20

191:12 198:14

211:7 238:6,21

241:8,17 242:6

242:22 243:4

243:12,21

244:20 245:14

249:8 250:13

251:12,20

257:3 261:1,2

261:3 262:4

263:4 265:18

266:5,17 267:7

268:4 271:1

274:11 277:7

277:10 279:22

282:10 283:8

283:16,17,22

284:12 294:7

297:6 319:13

321:18 322:4

326:2 330:3

331:15 332:3

334:1 342:1

343:3 351:15

351:20 354:17

358:8 366:21

379:9 382:18

doj 219:16

224:9,18 239:1

239:20

dollars 141:8,9

domestic 113:22

160:4 161:22

217:19,21

365:16

dominant

144:16

dont 11:17

13:22 15:14

17:8,11 21:8

22:10,13,17

23:7 25:22

26:3,16 43:14

44:9 47:20

48:6 49:22

51:12 53:5,10

55:7 56:16,22

58:5 59:16

64:8 68:2

70:13,19 73:12

78:2,13 79:14

82:12 83:6

101:1,19

102:18 104:13

106:15 107:16

120:15 123:1,2

132:21 137:17

143:3 144:9

148:22 149:20

152:4 155:4,21

157:13,14

158:10 160:20

162:13 163:5

165:4 166:13

166:13 170:1,2

171:1 173:11

178:5,22 179:2

182:3,21

188:18 189:11

189:12,22

190:9 191:4

193:9,14,16,17

194:2,3 196:15

198:6 201:7,13

218:4 220:21

223:22 239:2

241:18 248:7

249:16 253:13

254:10,14

256:11,16

258:19 261:15

267:10 270:21

272:20 273:4

274:4,7,9,21

274:22 275:14

275:16,18

276:12 277:2

279:6,8 283:19

284:7 288:16

289:21 290:20

291:6 297:3

307:21 309:19

309:20 311:15

317:22 318:16

328:10 329:17

329:18 332:8

332:18,20

334:4 335:8

336:20 338:11
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

19

339:18 342:11

344:2,16

345:11 349:9

354:3 356:20

357:1 359:12

360:9,14 361:2

367:14 371:21

376:8 377:5,6

377:19 381:10

381:13,14

door 14:3

113:16 120:13

173:3 194:20

doortodoor

14:16

dossier 49:2

doubting 351:14

369:11

doubts 243:3

downsides

282:22

downstream

36:12 334:7

335:6

downtown

248:1

dozen 360:19

dr 160:1 189:10

drafters 53:13

64:21

dragnet 67:14

draw 184:2

280:10

drawer 306:15

drawing 255:20

drawn 259:18

draws 102:11

278:16

dream 303:21

drive 57:1 63:17

148:19 172:10

382:3

driven 188:6

229:16 336:16

driving 56:10,14

117:22 188:14

drones 15:6

369:16

dropped 16:20

duck 279:6

duckduckgo

198:13

dupree 7:15

286:15

dust 75:1

dusty 306:16

duties 205:14

dynamic 308:2

dynamically

348:20

E

e 229:22 230:1

e0 278:22

ear 164:20

earlier 57:15

79:2 101:21

129:15 142:22

144:21 161:2

179:22 194:8

194:19 199:19

200:4 203:10

226:16 246:1

310:5 365:8

early 143:18

easier 38:18

39:1 112:1

141:4 145:10

324:22 325:7

349:15

easily 75:1

162:4

easy 133:12

193:18 328:22

349:18

eat 86:15

eavesdropper

39:10,13,17

ecommerce

206:17

economic

138:17

economy 289:19

291:20

ed 2:12 33:11

75:12 77:11

87:10 101:11

105:10 115:21

182:4

edge 270:9

eds 86:10

education 225:9

311:10 356:15

357:2

educations

316:12

effect 26:6 34:19

35:14 36:14,18

66:10 79:2

80:12 168:5

169:2 315:17

325:9 330:3,19

360:22 379:12

effective 35:17

41:8 67:14

69:19 83:9

93:1 94:20

114:4,12

130:12 171:18

173:21,21

174:14 188:21

192:18 208:12

210:11 213:22

246:15 276:19

283:5 292:17

311:7 312:18

312:21,22

319:18 321:12

321:21 322:15

342:7 366:19

effectively

118:12 310:1

310:13 356:8

effectiveness

319:20 377:2

effects 36:12

37:2 291:8

312:22 366:14

effectuate 11:2

efficacy 213:20

380:6

efficient 294:21

356:19

effort 87:2

118:12 323:12

efforts 7:17 32:6

207:19 217:2,8

217:9 238:14

egaurdian

220:21

egovernment

240:21

eguardian

220:15 221:2

eight 288:11

either 21:5 61:4

62:6 66:11

67:1 72:21

77:13 98:13

103:5 115:9

183:15 245:3

261:14 264:7

264:22 281:6

282:3 283:20

291:22 294:6

328:8 351:14

364:16

elaborate 38:21

69:2 121:6

162:18 217:4

electronic 290:6

308:10 319:2

element 64:1

128:22 323:19

324:10 346:11

elements 231:17

345:20

elephant 201:14

eliminate 56:16

56:22

elisebeth 4:14

elizabeth 2:7

elses 51:18

elusive 235:2

email 132:12

188:21 343:17

343:19,22

embed 354:13

356:13

embedded 30:2

127:1 218:6

220:5 224:5

230:8

embodied 80:21

embrace 152:15

emergency

99:12

emerging

171:20 197:6

290:14

emitting 142:5

emphasis

100:16 101:12

300:3

emphasize

113:8 327:15

emphasizes

108:1,14

employ 326:22

employee 95:12

343:14,17,18

343:21

employees 95:17

107:14 126:8

172:13

employer 339:6

339:16

employment

37:13 59:17

60:4 293:1
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

20

enable 37:12

248:4

enabled 103:6

135:11

enables 27:16

35:11 129:2

enabling 28:11

129:16

enact 128:19

encompass 12:3

171:3

encompassed

42:8

encompasses

8:2 12:12

152:5

encounter 362:9

encourage

115:15 118:6,7

118:18 154:21

384:2

encouraged 7:1

encouraging

128:16

encroach 362:3

encroached

364:20

encroachment

317:9 365:19

encrypt 24:3

162:6 189:12

encrypted

190:14,20

encryption

111:8 112:4,5

112:13 115:16

131:10 159:4

165:20 166:13

185:12,13,20

185:22 186:12

187:4 188:2,3

188:4,5 190:10

190:11 191:2,3

196:4,5 234:10

383:13

endangering

200:11

endeavor 56:18

ends 108:16

361:18

endurance

285:1

enduring

133:16

enemies 44:14

55:18 115:10

enemy 114:7

160:2

energy 347:7

enforce 135:12

155:5 158:4

174:13

enforceable

135:5 140:14

140:17 158:6

169:11

enforced 171:22

enforcement

38:22 69:10

70:16 112:7,18

132:10,22

133:20 168:4

169:1 189:16

191:6 217:3

219:18 220:18

221:7 224:14

282:3 304:6

enforcing

140:15 342:6

engage 248:8

276:14 319:5

363:15,18

engages 218:12

281:17

engaging 52:8

72:15 199:3

engender 57:2

engine 198:13

198:15

engineer 176:21

303:10 304:18

357:11,11,12

engineering

26:4 305:6

311:17

engineers 127:5

143:15 172:15

249:4 304:22

305:11 309:20

311:14,15,18

311:22 338:18

356:16 374:19

enhance 210:16

215:2 256:2

enhanced 86:13

195:17 298:16

enhancing

27:13 144:7

348:15

enjoy 133:11

enormous

117:22 293:8

320:19

enshrined 8:12

ensure 24:16

57:22 87:3

93:9 114:1

126:4,5 133:4

171:19 173:4

173:11 192:15

205:9 216:4

226:20 232:15

241:6,6 246:9

ensuring 209:5

245:20 295:11

296:2

enterprise 274:4

enterprises

125:10

enterprize

273:20

entire 13:10

73:14 220:22

301:17

entirely 25:13

114:20 297:19

entities 24:4

39:8 42:19

43:22 44:9,17

91:6,6 209:7

209:14,18

221:3

entity 102:15

106:8 230:1

302:15 340:14

entitys 102:14

enumerate

138:1

environment

217:14 228:11

289:10 325:14

329:11 330:6

331:3,4 353:4

353:5

environments

346:6

epic 375:4

epicenter

124:13

epidemics 180:9

equally 41:5

equate 13:17

equation 210:15

213:15 214:2

267:6 382:3

equipment

228:15

equipped

377:16

era 186:4,4

228:2 234:20

erika 3:13

215:10 225:8

239:10 244:17

245:8 255:17

264:6 280:20

310:6

erosion 115:12

errand 381:5

error 31:6 55:9

56:7,17,19

57:1,6,13

58:16 59:21

84:16 87:5

174:15

errors 56:11,14

87:1

escalation 127:3

especially 37:16

61:7 63:22

121:22 133:10

143:9 155:18

180:9 192:6,10

280:5 290:15

310:15 322:14

328:17 338:16

349:16 357:9

essence 233:3

essentailly

265:21

essential 25:16

31:14 59:10

64:2 111:8

125:5 316:12

346:13 374:7

essentially 25:17

74:17 90:3

168:17,19

169:21 195:9

314:11,16

315:19,22

316:7,9 324:3

324:15 325:2

326:2 335:16

335:17 345:16

345:21 360:22

362:6,22

365:11

establish 146:8

206:18
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

21

established

210:8 313:13

establishing

135:9

establishment

89:10

esteemed 140:12

estimate 164:4

etcetera 19:8

54:5 64:9

91:10 153:4

169:7 178:11

188:5 254:12

279:10 313:1

348:6

ethical 172:13

379:18

ethicists 237:12

ethics 294:6

311:17

ethnic 122:1

ethos 335:12,14

europe 290:15

343:5

evaluate 23:6,12

24:21,22 225:2

236:10

evaluates 73:4

216:1

evaluating

235:7 296:6,19

332:1

evaluation

263:16 332:10

333:8

event 7:17

137:17 208:8

events 123:6

323:1

eventually

379:14

everybody

51:18 53:11

64:7,16 67:18

76:3 77:4 85:4

98:18 137:6

241:22 270:5

274:1 289:20

304:3 320:17

321:8 353:19

367:2,18

everyday 36:1

everyones

147:18 195:4

285:1

evidence 67:12

221:19,20

222:3

evidentary

272:15

evidentiary

221:22 275:2

evolution

124:14

evolves 134:3

149:16 363:8

evolving 196:19

196:20 226:10

exact 275:6

exactly 57:4

66:10 77:9

88:19 89:13

147:14 214:14

307:7 308:11

308:13 310:11

310:13 320:20

339:18

examined

281:12

example 27:12

29:4 34:15

35:7,19 37:4

38:18 39:8

52:11 59:1

82:5 86:22

99:8 113:22

126:11 145:12

145:15 148:2

160:8 161:21

170:9 173:18

174:6 175:15

180:18 195:12

196:3 210:7

212:6 218:9,20

229:10 231:19

239:11 240:19

243:14 245:18

250:6 269:15

270:5 271:17

281:17 290:4

301:3 320:7

329:3 343:9

349:18 351:17

353:12 369:5

examples 35:19

35:20 36:3

58:13 122:4

exante 70:18

excellent 66:17

171:11 296:1

exception 14:8

99:10 168:18

exceptions

53:16 65:4,12

65:15 71:21

295:3 344:1

excessive 365:19

exciting 226:8

exclusion 48:11

exclusive 309:11

exclusively

131:22

excuse 145:14

317:22

executive 117:4

127:9 193:1

212:22 227:9

229:18 279:1

301:5

exemplifies

301:12

exempt 222:13

exemption

14:13

exercise 15:9

192:19 207:19

210:5 276:15

328:12 340:14

exercises 192:12

325:4

exercising

107:11 209:16

exfiltration

343:12

exhaustive

380:16

exist 55:16

80:12 136:21

167:10 309:8

existed 136:12

309:6

existing 71:16

128:18 132:19

233:14 281:8

exists 158:14

302:22 304:18

expand 115:19

149:4 167:11

198:9 282:8

368:12

expanded 188:2

217:15 304:7

306:7,10

expands 231:15

expansions

217:16

expect 5:14 36:2

40:7,8,8,9

119:22 148:19

148:22 149:10

149:12 166:22

167:9 191:13

366:15 368:14

expectation

74:17 147:6

148:14 149:4

152:21 157:16

166:19 191:22

197:7 198:7

334:11 362:4

363:7,10,20

364:3,18,21,22

366:5,17

368:13,20

369:2,6,12,15

369:21 370:6

expectations

47:18 147:9,11

149:13 150:9

151:19 167:3

196:20 226:12

323:15 344:15

346:18 347:4

380:14

expected 47:14

148:7 212:14

expecting 98:18

experience

98:17 154:18

216:11,11

242:17 243:5

244:16 314:15

317:16 321:4

325:12 347:2

experiment

54:19 55:14

experiments

250:1

expert 61:11

225:14 256:7

380:11

expertise 126:22

184:8 244:15

249:20

experts 41:10

110:16 178:12

237:12 249:14

374:22 376:15

378:7

expired 80:13
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

22

expires 385:18

explain 22:10,13

147:13 160:7

218:5 283:22

284:4 375:10

378:7,15

explained 248:6

375:4

explicit 98:8

345:16

explicitly 34:8

252:18

exploited 378:1

exploiting 39:17

exploits 111:20

166:9 170:7

exploration 21:4

explore 7:21

339:8

exploring

285:15

explosion 138:3

expose 49:12

171:2 228:22

exposed 47:20

47:21

expost 70:17

87:4

express 30:16

327:22

expression 21:3

expressly 204:3

exquisite 380:8

extended 15:2

304:3

extensive 7:2

74:1 230:4

extent 12:14

52:16,17 86:16

89:5 123:10

142:17 151:5

174:12 188:13

196:19 213:21

213:22 222:14

245:17 260:11

266:10 276:4

302:4 340:18

363:8 378:5

external 244:14

346:12

externally

346:16

extra 87:4

177:16 205:21

294:19

extract 91:11

extraordinarily

117:15

extreme 108:12

115:8

extremely

108:20 161:1

197:12 204:5

207:14 276:21

361:6 378:2

eye 105:5

eyes 364:19

370:22

F

face 26:1 36:9

100:19 268:15

facebook 17:10

78:15 136:21

191:17 197:2

198:21 199:1

339:1 352:19

facial 148:3

facilitates 129:2

facing 344:6

fact 13:22 14:14

19:22 24:15

25:15 37:7

43:14 48:11,22

49:6,13 71:20

75:6 78:8,14

79:16 88:18

98:19 99:2

118:16 119:1,7

120:20 122:4

133:2 143:10

143:19 145:1

147:10,10

153:8 170:6

175:11,12

177:19,22

184:10 190:13

193:11 197:3

197:14 198:2

199:6 212:18

213:7 223:10

223:12 224:12

243:10 258:2

279:14,19

280:4,17 292:8

292:18 315:9

318:2 332:7

346:19 352:2

373:18 376:13

factored 177:11

factors 94:19

250:9

facts 5:7 34:3

35:6 47:8 48:3

factual 219:1,9

fading 150:10

fail 37:22 38:9

fails 299:16

failure 38:5

58:12

failures 38:3

58:6 95:15,19

fair 67:16 100:2

100:17 125:20

134:6 149:7

204:8 225:14

231:20 296:8

301:19 303:3

336:12

fairly 58:15

152:10 240:21

244:21 245:9

246:5 305:17

faith 256:20

fall 65:12 95:6

300:10 344:15

falls 43:5 180:20

false 5:9 56:12

56:12,13 58:16

familiar 30:8

304:2

familiarity

377:21

families 122:9

famous 35:7

fan 190:11

far 9:6 28:13

29:11 75:17

123:21 168:10

218:4 278:14

288:14 291:2

317:19,20

fast 277:22

371:22

faster 117:18

138:10,11

169:13

fathers 8:11

fathom 139:19

favor 4:17

384:11

favorites 136:6

fbi 81:4 112:9

217:8,19 218:1

218:12,19

223:3,15

225:10 239:11

239:16 240:2

240:13 241:3

246:19 248:19

281:14 306:13

fbis 217:14

225:9 252:3

fear 28:13 56:2

382:14

feared 16:15

feasible 84:3

92:11 115:13

181:19,22,22

359:17

feature 365:10

374:8

features 175:11

188:15

federal 4:10

30:8 122:11

132:11 205:3

216:12,14

220:19

feedback 183:20

211:12 352:18

feel 29:9 41:20

54:18 128:10

177:22 207:11

240:8 243:1

260:20 293:10

355:8,21

feeling 16:6

54:20

feelings 51:11

fellow 9:18

25:19 74:11

206:2 252:16

286:10

fellowships

311:11

felt 243:11,17

269:11 339:12

377:11

felten 2:12

33:11,17 60:22

61:19 63:2

67:21 79:6

80:2,5,7 86:16

89:20 90:13

95:15 105:10

105:19 115:21

169:17 200:4

fewer 320:22

fiction 13:20
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

23

80:9,11,11

field 61:13 64:3

155:18,20

176:10 223:7

249:14

fifteen 149:6

fifth 131:14

294:8

fight 133:3

217:2

fighting 111:9

figure 62:10

84:22 123:20

159:10 162:11

190:7,8 214:3

261:22 262:1

313:21 316:9

316:16 340:4

379:19

figured 191:11

254:16

figuring 191:13

191:15 312:21

file 34:22 74:22

151:10 152:14

222:2 306:13

306:19,22

307:5,7,8

filed 129:10

files 34:14,17,19

35:3

filings 375:12,12

fill 334:12

377:18

filled 283:13

filter 188:22

final 6:6 39:21

134:14 285:3

339:19 372:16

finally 7:13 95:5

114:15 119:21

145:22

financial 304:9

336:21

find 26:12 38:7

117:19 161:6

184:12 215:1

241:8 244:20

257:5,6 267:21

271:1 311:6

318:11 319:16

345:11 347:9

349:4 361:8

finding 116:11

fine 88:5 105:18

159:6,7 182:18

367:2

fingerprint

170:11,13,20

171:1

fingers 172:21

finish 324:20

fipp 326:15,22

327:3,10 328:7

328:14

fipps 25:21 26:5

80:21 81:7,8

82:2,7,12 83:5

83:17,18,22

84:1,6,12 86:3

86:17 87:20

88:3,20 99:22

100:8 101:18

118:6,6 119:4

119:7,14

152:17 162:17

271:18 288:3,8

288:12,19

289:7,21 290:1

291:18 296:18

296:22 297:8

297:16 299:17

299:20 300:4

326:8,14,16,19

328:19 329:4,4

330:2 331:8,20

332:3,8,15

333:4 335:13

350:8 354:8

370:3,7 381:11

381:13,14

382:19 383:12

fippslike 289:15

316:3

fips 220:1 221:5

222:18 224:3

231:21 232:1,7

233:13 260:17

261:11 271:9

271:15,16

272:3 273:1,15

274:21 277:3

282:1

fire 357:14

firewall 179:5

firm 124:22

248:1 267:15

first 5:21 7:18

13:3 18:7 34:1

34:5 36:6

55:10 61:20

62:7,14 65:17

71:4 76:18

80:16 94:22

99:22 102:9

110:11 111:7

114:18 118:20

119:2 120:4,8

121:5 124:22

128:7 142:1,9

142:12 148:13

158:8 159:22

162:20 171:12

179:20 186:13

189:9 199:10

206:21 226:6

228:1 231:19

235:14 242:1,7

242:19 253:22

255:21 274:4

276:1 281:19

285:13 287:13

288:21 297:13

300:2 313:12

313:17 314:11

318:20 322:17

323:7 326:10

333:12 344:22

345:13 346:3

348:7 352:5

358:15,16

361:13 364:5

381:12

fisa 129:8,17

130:4,11,14

179:15,20

229:19 245:18

301:21 352:12

358:18,22

373:7 376:21

fisc 164:18

165:3 353:13

374:10

fisma 320:17

fit 58:9 62:7

97:22 206:7

322:11

fits 50:1 101:19

261:10,11

five 4:12 18:6

76:1 139:7

141:20 159:16

177:1 235:10

278:9 286:10

322:9

fix 57:12 305:2

305:7

flannel 164:22

flashlight

350:15

flat 136:21

flatirons 311:12

flaw 253:1

flawed 71:18

flaws 83:5

flexibility 38:15

flick 348:16

flip 371:17

flipped 359:15

flow 105:13

142:15 143:21

143:22 156:4

flowing 185:22

flows 40:16 68:6

186:6 242:2

308:11,11

319:2

fluid 237:8

flush 359:7

flushing 360:8

flying 194:2,3

focal 238:11

277:12

focus 5:21 12:10

38:13 46:13

94:19,20 95:14

111:6 120:3

131:22 150:3

217:7,8 228:3

230:18 238:14

243:12 244:12

244:14 256:2

258:21 264:9

268:18 289:16

289:21 290:17

292:1 293:12

294:12 305:13

323:18 361:15

361:22 362:8

382:2,6

focused 31:5

127:18,20

128:11 131:15

131:17 177:3

209:5 241:21

242:20 244:6

248:2 260:19

285:19 290:6

292:14 293:7

296:16 352:9
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

24

353:7,11,20

focuses 12:15

295:17 338:3

focusing 211:6

224:14 281:14

341:20 350:9

350:10 382:20

foia 14:6,8

folks 178:20,22

193:19,21,22

196:22 203:10

205:19,21

235:20 238:22

251:14 255:3

269:3 283:15

285:20

follow 65:8

126:8 173:17

189:13,13

205:21 211:18

211:22 212:14

213:8,9 232:8

298:6 299:17

followed 9:17

12:7

following

138:20 140:11

185:15 211:20

212:18 213:5

242:22 260:15

344:21 363:3

follows 36:19

43:10 98:12

followup 245:7

263:14 354:6

377:6

food 276:22

fools 381:5

footprint 246:6

footprints 240:2

force 51:9 61:21

89:21 90:3

195:5 263:21

forcefully

372:12

forces 346:22

376:18

forcible 120:20

forcing 263:15

268:4

forecasting

381:4

foreign 111:16

115:3 179:15

212:10,21

227:6,7,16

228:3,5,12,16

229:18 232:6

256:12 301:5

358:7 381:21

foremost 313:17

322:17 344:22

346:9 361:13

364:5

forget 23:16

form 195:17

225:4 233:1,19

236:16 242:9

270:14

format 9:9

former 3:20

211:21 313:6

373:4

formerly 25:7

122:6 376:19

forming 145:2

forseeable

221:22

forth 268:17

284:15 318:16

374:11

forthcoming

108:21

fortunately

288:8

forum 1:5,12

205:1 224:12

264:3

forward 41:14

96:15 116:7,22

141:1,15 215:6

269:20 270:10

305:16 352:16

383:18

foster 29:7

127:16 129:20

130:4

fostering 27:9

fosters 27:1,17

found 88:15

92:22 171:18

173:20 235:1

246:22 256:9

316:13 323:8

foundation

135:9 137:20

foundational

54:9

founded 23:4

25:20

founder 25:5

33:13

founders 304:16

founding 8:11

four 5:20 118:19

236:21 313:15

317:14 357:15

fourth 12:20

14:22 22:21

27:22 29:13

42:2,14 43:6

52:9 53:7,14

64:13,22 71:15

71:17,22 72:5

72:6,8,20

80:19 81:8

82:1,2 103:18

107:4,9 119:21

121:5 130:16

167:13,19

168:18 229:16

292:5,13

293:13,14,18

294:3,5 314:12

350:12 353:12

353:14 368:22

372:15 383:12

framed 229:20

366:1

framers 313:19

framework 46:1

82:13,17 100:8

100:9,12,20,21

132:17 154:9

205:22 231:21

232:1 274:3

296:10,19

297:1 326:20

327:12,17

332:1,15 333:5

333:6 338:12

368:21 369:7

369:12,21,22

370:4 372:5,7

379:21

frameworks

204:8 231:16

296:12 333:7

framing 265:14

framwork

211:18 237:1

frankly 56:11

288:3 289:18

290:1 319:3

325:8 329:10

franzen 8:3

fred 3:18 350:6

350:6

free 20:9,9 22:6

22:6 41:21

54:19 172:3

241:14 355:7

364:7

freedom 5:4,4,5

5:8,9,12 11:2

11:16 14:6

22:20 27:13

212:6 266:21

freedoms 133:9

freely 22:18

127:13

frequently

228:13 238:2

238:22 248:16

288:14

friedman

136:19

friend 13:8,14

friends 17:11,12

70:1

frog 347:10,12

front 6:22 9:11

14:2 85:21

109:22 145:6

203:17 207:6

241:10 254:10

286:3 303:21

365:20

frontend 245:16

300:6,14 359:2

359:3,16 371:9

372:6

fruit 305:17

frustrated

373:13

frustrates 38:4

38:5

frustrating 86:7

ftc 264:8

fulfill 300:18

fulfilled 254:7

full 22:11 76:3

116:20 123:10

156:3 168:21

205:6 209:6

351:19

fulltime 244:12

248:8 249:20

fully 38:1 84:12

100:11 189:20
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

25

191:14 208:17

210:16

fun 275:9

function 17:2

207:4 254:6,17

259:2

functionally

14:3

functioning

245:21

functions

104:22 207:20

217:14 252:4

254:3 273:5

298:8

fundamental

132:2 226:21

272:18 287:18

334:8

fundamentally

69:7 232:22

234:2 297:22

further 34:21

35:15 86:20

91:15 127:4

132:15 225:20

266:10 282:13

354:3 362:10

385:9

future 57:17

128:20 138:8

143:6 181:18

221:22 227:4

233:15 262:22

293:21

G

gained 288:8

gains 27:2

gap 156:14

324:17

gather 72:22

74:20 105:1

271:11,15

gathered 22:4

48:4 49:15

78:7

gathering 20:15

20:20 47:3

71:10,13 72:16

75:1 180:6

381:22

gathers 22:2

gay 122:19

123:13

geiger 3:19

295:14,14,20

326:8,12 327:2

331:19 340:8,9

358:15 368:16

371:3 375:16

377:13,19

general 29:1

30:10 42:4

95:4 96:3

112:10 209:11

209:12 215:13

215:17 217:19

238:3,7,15

239:12 240:1

243:15,15

244:7,7 257:17

257:19 290:16

generalist

248:12

generalized 84:8

generally 19:18

69:6 90:10

238:15 265:15

299:12 360:2

generals 295:6

generated

182:18

generating

139:18 142:5

182:17

generation

156:14 157:14

158:15 191:11

191:21

generations

341:14

gentleman 9:11

gently 203:20

genuinely

259:15

geogretown 3:4

george 2:15 18:3

georgetown

1:12 4:7 117:5

georgia 3:3

110:14 143:10

german 343:17

germany 343:20

getting 44:4

45:1 52:8

82:18 110:22

143:1 184:21

187:10 347:10

gewercman 7:16

giant 343:13

give 9:11 33:14

58:13 71:5,6

105:16 120:14

143:13 154:19

170:8 180:18

229:13 274:21

308:5 333:16

333:17 335:2

344:17 364:7

given 5:14 23:17

90:7 111:11

114:22 119:10

120:2 179:4

205:20 233:4,4

246:6 250:7

253:7 265:7

273:1 274:11

297:10 319:17

334:8 358:11

373:16 383:16

gives 21:12

64:14 270:5

giving 87:11

97:14 249:12

347:20

glad 88:5

global 131:18

206:19

globally 200:15

globe 154:1

glove 322:19

go 8:10 17:9

22:10,13 28:22

43:9 48:20

51:2 54:5

57:12,18 67:19

75:20 79:19

87:15,17,19,22

88:8 103:8

105:10 109:15

109:17 141:16

142:8 146:21

148:18,19

149:2 152:18

159:17 168:9

168:19 173:3

176:15 178:7

181:8 184:12

184:16 185:4,6

186:21 196:13

201:21 202:2,6

202:8 207:5

220:21 223:7

238:1 245:17

261:3 262:13

263:17 269:20

274:1 278:10

285:6 286:17

286:20,22

287:19 298:9

312:8 320:18

331:18 335:9

346:19 347:18

350:6 353:16

357:15 372:11

373:20 374:3

380:20,21

goal 268:22

301:6 349:10

goals 86:20

114:17

god 355:3,5

goes 10:11 21:19

91:16 155:20

171:12 181:2

201:19 211:9

211:16 278:16

351:4 356:14

going 10:4 23:7

23:22 24:7,21

24:22 25:1

41:16,22 63:19

66:10 74:10

75:22 77:19,20

80:17 84:9,21

92:20 101:3

102:4,4,17

120:16 138:11

138:12,13,14

139:17 140:7

141:3,4 145:8

145:9,21,22

149:18 156:17

164:21 165:1

165:15 166:16

169:13,13

174:2,8 177:11

177:16 178:21

182:20 183:6

184:19 185:11

186:3 187:18

189:14,18,21

189:22 191:14

193:19 195:22

197:8 199:21

200:4 202:11

207:4 212:5

215:2 233:20

234:18 241:16
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

26

245:1 250:1,4

251:5,8,11

252:2 253:5

255:7 257:10

258:14 259:13

259:18 260:9

260:10,13,17

261:6,20

263:11,13

265:21 269:13

270:2,9,16

272:7 276:14

279:7,9 283:5

283:19 286:21

311:19,19,20

312:1,10

316:12 319:8

322:3 323:17

324:13 330:9

339:10 342:3

348:17 349:19

352:16 353:1

355:11,22

356:6 357:7,8

357:13,14,20

359:20 360:10

361:8 362:16

363:19 370:7,8

371:22 372:1

378:21 379:13

380:20,21

goitein 2:13

10:6,9 42:13

44:2 51:1,3,19

53:3,5 63:22

64:19 78:3

80:18 81:14

82:19,22 87:9

97:9 102:10,17

103:10,22

golden 186:6

good 4:2 18:5

27:10 40:5

49:6 53:9,11

60:21 85:19

88:12 100:6

107:19 109:9

109:10 110:19

113:17,17

114:8 115:12

116:22 117:9

129:20 155:8

157:20 158:2

159:11,21

160:3,5,9

162:3 165:20

176:2 177:6

182:11 194:20

197:6 198:6

203:4 207:5

245:22 256:20

263:5 275:1

277:14,15

279:5 284:22

294:22 321:16

321:17 327:5

360:15

google 43:16

77:19 78:14

112:3 339:13

googles 108:7

gotten 37:15

322:22 337:17

gov 7:8,11 384:5

384:9

governing 221:1

government

3:10 6:3 8:21

11:1 13:15

14:8 15:6,18

15:20,22 16:2

20:14,19 21:21

21:21 22:2

23:5,6,12,19

24:4,11 27:14

27:21 28:1

29:22 30:7,18

39:7 42:15,16

43:15,21 44:8

45:9 47:3 53:8

54:21 55:16

56:16,18 58:1

59:17 61:2

63:1,3 65:1

69:8,19 70:2

70:10 72:15

73:3 74:19

75:7,8,14,18

77:21 78:8,11

78:17,20 79:19

79:20,22 81:9

81:20 85:5

88:21 90:2

93:3,15 94:21

95:12 102:11

102:14 103:14

103:17 104:3

104:19,22

105:2 107:12

108:4,6,12

111:18 116:11

117:13,14

118:1 122:11

124:7 127:13

127:15,19

128:5 129:5,11

129:16 130:6

130:16 131:1,5

131:7,12,14,16

150:13 153:16

157:2 167:16

167:20,22

175:12,14

176:4,4,6,15

176:18 178:1

178:15 179:1,6

179:10 180:14

181:1 186:10

186:16,20

187:3 189:5

191:7 192:9

199:16 200:1

200:16 202:9

202:10,16,17

202:18 203:2,7

204:1,20

206:14 216:11

230:17 231:17

233:13 241:13

258:15,17,19

259:1 263:4

264:20 266:22

267:18 272:2

273:7 285:17

294:14,22

295:18 296:10

297:12,18

298:18 299:17

299:19 300:2,8

301:9 302:3,6

302:12,18

303:2 314:6,10

315:2,7 317:7

317:17,18,22

318:1,2 322:15

325:12 327:20

334:16 337:6,7

337:10,13,19

337:20 338:6

339:14,15

341:21 342:8

343:1 344:9

345:2,15

360:17,22

361:4 366:15

368:7,8 376:10

379:19 380:15

382:15 383:9

governmental

12:6 85:3 86:5

94:17 97:7

107:11

governmento...

228:14

governments

45:1 57:21

132:4,15 133:3

188:12 200:8

258:14 293:9

298:8 317:4

318:4 380:6

governmentwi...

209:16

gps 15:7

grabbed 93:10

gradations

308:1

grade 283:14

grand 54:7

grant 301:20

303:9,9,15

318:19 319:21

338:10,11

342:9,10 348:7

348:13 356:12

362:22 366:11

378:12

granted 55:20

221:15 322:18

323:5

granular 11:14

101:13 210:10

307:16 309:1

342:19 348:10

grateful 313:13

great 42:22 43:1

43:17 74:14

75:8 105:15

116:17 143:5

155:14,15

160:12 180:8

234:12 238:9

239:7 250:4

313:8 334:14

344:13 368:4

369:5 380:12

382:18

greater 47:10

62:4,5 83:13

84:5 86:19
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

27

87:2 104:3

130:5 204:15

211:8 212:1

213:6,14

237:15 290:17

300:3 301:10

302:4 303:6

337:11 376:20

greatly 176:19

greece 166:7

green 9:12

207:5

grind 45:3

grocery 148:18

ground 28:11

31:19,20 32:19

250:8 376:3

groundbased

369:17

grounds 311:5

332:6

group 3:20 25:6

116:1 161:15

313:5

groups 41:10

251:1 336:10

growing 40:17

62:15 147:12

185:15 226:10

grown 104:21

growth 142:15

142:20 143:3

guarantee 107:1

107:3 138:9,9

141:2

guarantees 42:9

138:7

guard 261:7

guarding 291:6

guess 61:8

161:18 165:21

167:18 186:21

189:6 194:18

195:1 224:1

253:22 256:6

257:12 271:21

273:10,14

279:8 326:16

343:8 344:3

354:4 358:13

guessed 305:8

guessing 156:8

guidance 159:8

162:11 216:3

218:1 312:7,13

guide 125:17

226:20 276:7

282:2 293:20

guided 379:17

guidelines

217:19 364:4

gun 70:3

guy 161:7

310:21

guys 139:5

171:10 188:10

244:9

H

habit 259:7

hack 130:17

hackers 158:15

187:5 188:10

hacking 130:20

131:2

hadi 3:7 134:14

134:17 142:2,9

154:17 164:19

169:8 182:2

189:7

hadnt 347:12

half 124:13

137:15,16

139:10 163:13

284:3 368:1

halfway 108:9

hand 58:18

68:19 110:9

199:19 318:11

322:19 385:12

handle 64:15

127:8

handling 37:15

231:11

hands 151:17

156:8

hanging 305:17

happen 15:11

105:8 224:18

251:17 270:21

300:11 348:17

happened 80:10

122:13 184:12

311:1

happening 24:1

80:10 139:13

140:11 144:10

144:13 153:20

154:6 175:1

241:18 252:20

255:2

happens 49:14

57:5 109:17

119:1 166:12

260:11 265:16

happy 59:3

75:20 237:18

278:10 326:8

hard 197:10

246:8 249:6

266:16 268:7

270:3 310:11

316:2 323:14

324:18 347:13

371:21 381:5

harder 140:10

195:20 314:3

317:11

hardwired

308:18

hardwiring

308:14

harlan 18:2

harley 3:19

295:14,14

337:9 358:1,8

358:11

harm 16:5 49:12

50:20 54:6

55:5 59:7

237:2

harmful 49:7

121:15,22

123:19 382:8

382:11

harmless 36:11

harmony 135:8

harms 16:22

50:5,7 52:6

58:10 76:15

92:20,21 291:7

331:17,17

336:9,20,21

340:17 350:17

harness 234:18

hasnt 58:7 74:4

97:5

hasten 32:4

hate 291:22

havent 87:18

90:10 238:19

283:21 381:20

hawk 183:22

haystack 257:4

257:6

head 323:17

324:16

headed 240:3

headon 297:11

heads 215:22

headway 154:8

health 37:13

180:7,8 270:3

healthcare

332:18

hear 5:16 6:3

17:7 18:11

43:11 69:1,2

150:6 201:1

heard 15:13

83:15 96:11,18

97:4,12 130:14

139:1,16 140:7

166:19 199:11

235:19 269:11

280:2 348:1

383:9

hearing 4:9,16

4:17 207:9,18

355:6 384:11

384:15

hearings 210:9

hearsay 99:10

heart 208:7

hearts 346:10

heavily 33:2

182:22 375:3

hedge 380:14

held 1:12 14:12

14:18 33:2

91:2 132:13

267:9 318:1

325:10

hell 33:14

help 41:11 85:8

105:1,1 116:1

128:5 133:3

160:12 161:6

180:4 195:22

200:16 226:11

237:7 249:18

271:3 289:21

313:16 326:2

335:17 340:4

342:13 358:19

377:3 378:14

382:5,10,17

helped 129:20

206:18

helpful 11:10
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

28

116:6 159:9

164:19 202:19

232:17 248:9

254:15 260:3

276:6 314:5

354:2 383:17

helping 147:22

162:11 251:17

helps 261:14,17

263:7 379:7

382:2,3

heres 267:17

hes 124:12

287:3

hesitate 80:2

heuristics

143:16

hew 162:21

hey 46:18

120:13 148:17

176:12 243:7

269:19 274:2

hide 18:11 19:6

166:2,6

hiding 18:8,15

high 29:16

70:12 106:2

119:16 134:16

163:5 170:16

193:7 196:1

289:14 305:14

306:1

higher 336:13

336:16

highest 29:12

33:2 152:20

highlight 96:10

highlighted

91:13

highlights

120:19

highly 183:17

hill 87:21

304:21

hindsight 123:5

hintze 3:6

124:11,15

134:9 149:5,12

151:4 152:3

153:18 168:3

168:22 171:17

172:5 174:12

179:22 180:13

180:22 181:4

185:21 188:1

198:18 201:6

hintzes 144:14

hipaa 99:19

145:17 229:12

332:21,22

historical

162:21 229:14

historically

122:3 229:15

233:18

history 44:12

58:3,8 137:18

231:20 284:2

326:6 354:3

hit 48:21 311:19

hits 259:11

hitting 118:4

hobby 271:8

hold 62:20

226:21 286:16

318:10 321:9

334:6 342:12

342:12 346:14

362:13

holder 37:5

holding 26:3

62:13 91:6

110:1 207:17

287:10 340:14

holdings 43:20

holds 237:1

286:4

holistic 233:1

home 14:22 42:2

113:17 141:11

195:4

homeland 25:8

100:5 104:6

226:2 290:4

303:12,13

homes 151:10

151:18 341:4

honest 107:7

honestly 87:18

342:14

honing 182:18

honor 259:9

313:9

honored 226:5

hoops 320:19

hope 9:19 41:4

88:16 126:8

143:3 172:21

193:9 199:11

213:3 225:19

284:9 343:3

hopefully

306:15,20

307:9 359:1

hoping 140:19

hops 91:9,10

horizontal

316:17

horrible 174:1

horse 271:8

host 69:19

224:10

hosting 226:5

hotel 1:13 4:7

hotter 347:11

hour 181:11

hours 139:20,21

140:2

house 170:2

369:18

housekeeping

203:12

houses 53:9

hr 127:7

huge 186:17

250:15,16

310:7,8 320:3

320:5 356:7

374:18

hugely 144:16

human 15:4

26:17 51:5,5,6

51:14,16,20

56:18 78:9

96:21 98:17

132:3 153:10

155:10 174:15

349:5 350:1

351:22

humandriven

348:21

humanly 56:15

hundred 43:3

56:4 93:17

139:20,20

140:2 141:9

159:13 198:15

379:1

hundreds

325:16

hurdle 282:11

hurt 160:21

hurts 112:18

hypothesis 56:3

hypothetical

55:15 173:19

I

ic 30:3 278:18

iccpr 51:7

iconic 323:11

ics 120:7

id 19:12 20:12

22:21 33:18

76:17 79:8

82:20 91:21

106:1 116:9

144:21 145:5

165:16 179:8

208:6 227:1

229:11,13

233:15 272:22

313:15 318:18

318:19 322:13

326:7,10

330:17 333:16

354:10

idea 50:17 108:6

113:18 147:18

162:3 239:3

269:2,21 276:9

287:10

ideas 17:4 21:4

152:15 171:14

291:3

identical 355:12

identifiable 37:8

113:8 229:8

identification

46:4 48:22

161:1 237:6

identified 3:10

6:5 203:22

identifier 34:17

identifiers 34:11

39:9,11,18

identify 9:3 10:4

36:1 68:7 90:5

163:22 182:8

197:6 214:3

232:17 235:16

236:3,17

270:18 382:7

identifying 36:9

39:14 49:3

183:15 196:12

196:14 198:7

381:17

identity 34:20

35:2,3 39:20
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

29

59:22 113:21

216:14

ideological

10:22

ideology 44:10

ignore 48:2

ignored 352:2

ii 122:7,18 187:6

ill 17:22 41:17

49:18 58:13

67:18 74:12

76:5 79:6

102:5 106:16

107:22 109:16

119:10 121:18

134:9 142:8

175:22 176:1,3

185:4,5 187:20

189:8 198:8

217:7 250:10

257:18,18

276:11 279:6

279:11 280:2

280:20 287:10

304:2 316:11

364:1 378:12

illegal 294:5

illegally 292:8,9

illuminative

74:2

illustrative

380:19

illustrious 10:4

im 10:4,19 11:12

28:12 30:8

41:16,20,22

55:1 63:19

64:7 68:4

69:21 73:8

75:20 80:19

81:15,15 82:11

82:14,18 83:8

85:18 87:12

98:10,18 101:5

102:3,4 107:8

107:18 108:11

118:18 134:22

135:1 137:12

140:10,19,22

149:1 153:18

160:11 165:15

166:20 167:1,5

167:8 168:9

171:5 174:1

182:4 189:15

190:11 193:9

197:2,8 201:11

201:20 207:3

220:7 231:13

237:18,18

241:11,14

242:12 247:10

253:5 255:7

259:5,21 262:6

263:10 269:6

270:16 271:21

277:14,15

278:10 279:8

287:9 288:5

289:8,12

303:20 304:22

309:3,9 313:8

318:6,16

326:17 328:22

330:9 333:14

343:21 350:21

350:21 351:14

357:12,14

360:10 367:2

370:11 372:12

381:13

image 28:9

68:10

imagine 87:2

306:12 378:3

imagined 86:4

imbalance

357:17

immediate

352:18

immediately

266:8,8

impact 3:2 6:1

17:1 109:13

121:11 172:18

175:7 204:10

220:12 224:21

236:18 240:19

289:18,19

290:5 291:19

296:14 332:5

375:5 382:11

impacted 107:4

266:22

impacts 30:18

118:21 119:8

204:21 205:6

266:13 291:8

291:17 336:9

336:19 381:17

381:18 382:8,8

382:10

impediment

178:18

implement

145:4 146:1

206:19 342:13

implementation

346:12

implemented

84:13 92:12,16

100:10,11

215:19 345:21

implementing

379:22

implements

348:4

implicates 121:5

implication

37:14 39:6

107:20

implications

36:6 76:13

89:2,17 152:15

153:5 185:11

370:19 371:2

implicit 98:11

282:17

implies 219:9

imply 98:7

importance

11:15 115:22

180:8 214:16

242:15 279:13

350:9

important 11:18

12:12 20:22

28:14 38:17

49:21 68:1

69:13 71:11

73:5,10 96:10

96:13,14 100:1

120:5 124:17

136:15 143:6

147:3 153:5

161:12,17,19

161:20 162:5

178:10 192:7,9

196:4 207:9,21

216:9,17

224:21 227:20

239:4 240:8

242:4 243:7

257:11 258:1

259:2 260:20

264:1,16

272:11,12,13

276:21 277:2

285:4 296:9

312:5 313:14

329:22 331:15

335:12 336:6

340:16,21

341:10 346:22

359:4 372:10

374:7 379:5

impose 29:16

72:21 81:9

95:7 239:16

325:2

imposed 322:19

323:4 324:6

346:16

imposing 69:9

impossible

55:20 77:18

86:18 164:3

330:5 373:16

impoverish 17:2

impression

17:18 200:3

imprison 104:1

improperly

49:13 94:5

improve 114:16

165:9 200:10

224:20 240:18

357:2

improved

111:13 224:7

improvement

309:6

improving

349:19

impulse 85:19

inaccurate

60:18

inaccurately

294:17

inadequate 57:7

332:14

inadvertent

31:7 174:21

inappropriate

98:7 265:20

350:18

incentive 104:1

195:3

inception

119:11
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

30

inching 369:10

inchoate 266:20

incident 127:2

220:16

incidental

262:20 279:3

incidents 316:14

include 21:2

47:3 115:19

231:15 234:8

324:3

included 51:16

141:12

includes 126:21

127:2,9 302:6

327:14

including 13:10

61:1 112:6

125:22 165:6

206:20 216:5

312:17 370:6

incorporate

234:19 310:1

incorporated

144:14

incorrectly

73:11

increase 50:15

128:21 130:10

141:3 169:15

increased 114:6

149:14 169:18

198:15

increasing 61:12

104:9 131:9

186:10,11

245:11

increasingly

104:20 108:5

151:8 188:11

incredible

351:20

incredibly 17:10

72:2 246:7

290:22 309:1,2

incremental

67:3

inculcating 95:1

independence

4:22

independent

29:17 31:1

41:10 57:20

59:9 60:3,10

73:2 92:2

315:14 325:21

373:22 374:1,2

374:9,16

index 110:7

indiana 3:18

287:3

indicated 364:2

indirect 115:11

indirectly 183:4

indispensable

296:19 333:6

individual 8:1

9:4 19:14,16

19:19 20:1,2,7

20:8 44:7 49:2

49:7 54:15

59:5 60:8,15

66:19 80:22

81:3 82:3,8,15

86:3 94:21

95:17 112:19

125:9 130:7

163:7 164:13

183:14 204:4

221:5,8 230:1

230:21 235:8

236:11 268:14

270:15 271:18

272:6,7 273:6

274:9 277:11

289:17 295:4

295:10 296:21

302:16,17

326:15,22

327:10,19

328:6,14 329:9

329:18 330:2

331:8,10,14

334:11 336:15

337:14 340:19

364:22

individualized

84:2

individuals 8:15

20:7 83:12

84:6 91:12

121:2 204:21

205:16 253:8

281:22 291:19

298:12 299:5

302:19 327:7

333:2 336:10

336:10 337:5

337:10 340:12

340:15 352:10

363:15 371:14

individuated

60:16

industrial 20:18

industries 104:7

industry 59:20

60:4,5 63:1,15

104:21 117:13

117:14 118:1

118:11 131:11

144:4 158:1

178:5,21 179:3

179:6 180:15

189:11 190:5

200:17 202:5,9

383:10

ineffective

208:19

ineluctably

69:18

inevitability

142:11

inevitable

178:13,20

179:1,3,10

200:1

inexorability

142:11

inexorable

142:4

infer 34:3 35:6,9

37:1 59:8

79:15,16

inference 35:14

37:3,8

inferences 35:13

35:16

inferred 35:11

40:21 152:9

inferring 35:18

infiltrate 112:1

infiltrations

16:18

infinite 308:5

influence 115:4

212:13 242:13

inform 5:18

226:11 261:14

261:17 278:18

365:4

informant 13:16

information

5:11 12:9,13

13:4,7,10 14:1

14:7,9,14,18

15:3,10,12

16:3 17:11,12

19:8 20:15

22:3,4,5 23:18

24:20 33:14

34:6,8,20 35:1

35:2,11 36:10

37:1,4 39:5,14

40:15,16 42:21

43:13,21 45:5

45:7 47:14,16

47:19 48:3,4

48:13,14,19

64:1,8 66:7,9

66:22 67:3

68:6 69:8 70:3

71:10 72:16,22

74:20 75:11,15

76:14 78:7,8

78:15,16,17

79:9,10,16,22

81:19,20 84:3

87:16 89:7,15

90:6,16 91:2

91:19 93:9,10

93:21 97:14,16

98:1,4 99:3

100:2,13

102:21 103:8

103:15 106:6,9

112:6 115:7,10

118:2,15 120:9

120:22 121:2

122:8,10

125:21 129:6

129:13 131:5

133:5 134:6

139:12 141:8

141:12 142:7

142:16,20

143:20,22

144:1 146:10

150:16 151:1,2

151:9 156:5

160:19 161:4

163:16 166:6

168:7 169:10

169:18 175:14

176:12 186:8

186:18 194:10

196:12,14

200:2 201:20

202:10 204:9

204:12,16

208:15 209:22
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

31

219:5,6 220:18

221:2 222:4,11

222:14,19

228:22,22

229:8 231:11

231:20 232:3

235:4,5,15,17

235:22 236:7,8

236:12,13

241:12 242:2

246:12 248:6

249:9,12

253:18,19

254:10 261:4

265:2,4,6,8,20

266:6,12,17,19

267:1,3,7

268:10,14,19

268:21 280:4,8

281:18 283:10

296:8 299:1

300:9 302:14

302:15,16,19

303:3 307:7,8

307:13 310:21

317:19,20

320:9,11,20

321:10,15

334:13 337:11

338:7,8,21

339:2,7,22

340:1,12,14

342:1,4 343:11

348:9 349:21

358:2,4,12

359:7,11,14,16

360:9 366:18

366:22 367:7

368:2 370:15

370:17 371:14

377:1

informative

383:8

informed 66:3

informing 145:7

informs 208:14

infrastructure

111:15 310:9

312:4,6 342:18

ingenuity 143:4

inglis 3:20 313:4

313:8 322:13

322:17 324:1

333:20 334:4

344:12 348:1

351:6 360:13

364:5 380:2

ingrained 118:5

inherency 55:11

inherent 26:17

61:5 104:9,13

133:16 235:7

279:21 339:22

inherently 20:18

26:20 279:17

initatives 240:8

initial 9:14

218:7 232:13

235:9 251:2

initially 9:10

90:15 271:12

initiatives 212:7

215:19 216:2

247:6

inject 255:17,18

injury 104:2

256:5

innocent 256:13

innocous 268:15

innocuous 47:6

innovative 39:3

inoperable

101:7

input 211:11

237:9

inputs 143:14

inside 94:16

176:15 197:4

211:9 239:5

242:10 244:1

325:9 334:16

339:3

insiders 115:3,6

insight 247:5

335:3

insights 320:12

insist 62:19

114:10

insofar 11:10

27:1,5 107:10

inspection 58:17

inspections

58:14

inspector 244:7

295:6

inspectors 30:10

95:4 209:12

instance 32:22

55:21 59:2

60:2 94:22

145:18 160:15

161:9,14

164:18 255:22

256:9

instances 28:15

29:9 71:22

159:7 161:9

195:15 378:1

instill 357:18

instilled 304:17

institute 66:17

institution 30:7

institutions 61:1

107:10 133:10

210:1

instrument

146:8

instrumental

26:20 54:2

97:1 98:20,20

instrumenting

165:8

insufficiently

299:12

intel 193:22

intellectual 5:3

intellectually

275:9

intellgence

215:4

intelligence 3:12

15:13 30:4,11

74:1 85:12

90:18,22

118:11 119:15

163:3 179:16

192:7 206:6,13

207:13,15

208:7,13,18,22

210:11 211:10

212:11,14,21

213:1 214:12

214:19 216:22

219:17 221:22

226:13 227:6

227:16 228:3,5

228:12 229:19

231:6 232:6

242:10,18

243:7 244:2,8

245:1 253:15

254:4 255:19

256:12 258:2

266:15,19

267:8,10

275:11 276:11

298:5 301:5

304:7 320:3

353:5 358:8

381:22

intend 313:20

intended 217:15

intent 243:21

intentional

174:18

intentionally

175:4 316:15

intentioned

204:11

intentions 38:6

interact 98:6

interacting

139:13

interaction

273:6

interactions

70:16

interactive

110:13

interest 8:22 9:1

12:7 19:13

20:1,2 29:2

33:7 66:21

67:2 73:17

91:22 97:15,15

98:4 153:10

155:15 211:17

229:3 256:22

279:12 280:19

361:21

interested 80:20

89:20 137:12

149:9 153:22

154:16 160:1

220:10 272:22

367:6,8 372:12

385:11

interesting 11:8

88:15 107:21

216:18 226:17

239:18 268:8

282:18 339:8

344:4 357:10

357:16 365:8

interests 2:10

3:2,10 5:21,22

6:4 8:1,14 9:4

9:8 12:12 21:1

45:21 49:18

61:5 66:19
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

32

73:13,14,19

79:4 97:3

101:14,16,20

109:12 121:6

151:3 203:22

204:4 211:2,3

231:11,13

256:3 264:4,10

264:11 285:15

295:10 334:2

340:17 367:12

interface 349:20

internal 30:2,10

57:21 95:20

173:1 193:2

224:16 244:17

246:10 247:9

300:9 341:9

346:13,14

365:9 366:22

367:7 372:22

372:22

internally 268:2

280:2,16

338:20

international

51:10 113:2

132:16 154:3,8

206:18

internet 43:17

115:1 131:2

139:1 143:11

143:12 160:17

185:17 186:5

335:21

internment

122:17

interpretation

301:11 314:21

interpretations

71:17 315:1

325:19

interpretative

356:9

interpreted

301:15 355:20

interpreting

352:20

interrupt 259:4

intersection

110:16

intersections

135:14

interspersed

228:17

intervening 54:5

55:4

intervention

60:10,10 77:13

interview

329:20

intimate 151:16

introduce

109:16 201:17

235:11

introduced

137:5

intruder 113:18

intrusion 5:6

29:15 58:19,20

intrusions

112:14

intrusive 218:10

218:16 219:3

221:17 261:2

262:17 341:2

364:10

inured 147:18

invades 120:21

invest 188:16

312:6 347:7

invested 316:20

investigation

218:7 219:10

221:21 352:7,9

investigations

217:21 219:8

219:14 221:9

223:10

investigative

219:12 223:8

281:15

investment

127:11

invitation

303:16

invite 206:2

invited 6:14

9:21 224:17

inviting 10:12

207:8 216:8

295:22

involve 122:5

200:21 201:3

215:3 218:22

273:6 279:17

308:19,20

involved 58:11

116:8 145:2,6

146:5 184:13

199:16 362:1

374:13

involvement

10:15

involves 18:19

18:20,21 49:1

122:1 279:20

iot 139:1

iphone 137:4

ireland 132:14

ironic 337:16

351:5

ironically

361:14

irreconcilable

55:8

irrelevant

350:11,21

irs 69:10

isnt 18:18 19:22

23:14 49:10

54:15 75:8

116:4 246:18

252:8,8 304:12

362:7 367:10

368:14 378:16

isolated 36:14

228:13 263:20

isolation 36:11

issue 21:18 49:8

59:5,6 87:15

113:6 125:3

131:19 177:7

179:18 193:5

193:18 196:8

229:9 249:3

287:18 309:18

320:5,13

329:22,22

334:22 357:1

378:16,17

issued 162:22

217:18,21

issues 5:19

10:15 30:18

63:11 67:22

75:4,19 100:17

107:5 110:16

124:20 134:12

135:16 150:4,6

153:8 154:4

171:21 172:14

173:4 176:20

205:2 224:16

225:15 240:7

247:2,18,18

248:22 276:4

284:19 285:7

289:17 293:22

295:17 352:15

353:14 379:7

issuing 29:18

item 36:7,8,20

36:21 201:15

items 34:2 36:15

91:11

iteration 271:16

iterative 263:13

277:18

itll 269:6 346:10

ive 10:14 13:9

31:4 43:8

70:14 88:15

124:21 144:6

208:17 212:19

214:13 242:6

251:20 256:16

261:9 273:11

274:6 288:8

290:3 293:13

357:15 367:21

ivy 197:4

J

jackpot 278:14

jail 44:18 58:22

james 2:6 4:14

january 132:7

japanese 122:7

122:16

jargon 290:21

jerusalem

140:19

jim 96:6 109:6

154:2 159:19

164:20,21,22

278:3

jims 107:18

job 12:10 27:10

42:22 43:1

59:19 95:13

156:21 171:14

177:6 224:18

226:17 240:15

243:12,16,18

244:12 267:5

280:14 283:2

304:18 305:5

319:15 324:21

325:6 349:15
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

33

357:15

jobs 244:4

joe 6:22 7:16

joel 3:11 206:4,8

207:1,7 241:11

241:19 247:15

253:15 257:18

257:21 258:8

259:6,9,12

260:1 262:6

264:14 275:22

279:5 310:5

john 18:2 303:9

315:9,10

316:11 348:7

356:11 366:8

join 316:17

joined 242:19

joining 205:16

206:14 225:22

joke 321:7

jonathan 8:3

jones 76:8 369:7

journalists

115:4

judge 7:19 10:9

25:9 29:18

80:18 179:12

352:12 372:8

373:4,15 374:5

377:11

judged 54:21

284:9

judgement

25:15

judgements

32:19

judges 352:12

360:20 373:11

373:13,18

377:20

judgment 26:19

27:1 33:6

214:7 257:8

270:20 274:22

judicary 254:11

judicial 29:12

42:7 60:9

70:22 73:3

85:21 106:22

130:9,12

168:13 221:13

282:8

judicialmade

369:3

judiciaries

32:20

judiciary 74:6

117:7 179:16

193:21 210:5

julian 66:16

jump 356:12

378:12

jumps 366:11

june 137:4

jurisdictions

220:20

jurisprudence

15:1 292:14

jury 54:8

justice 2:13 3:13

133:13 209:15

213:2 215:12

222:7 246:7

364:15 372:15

justices 225:5

justification

63:12 72:22

justified 260:16

366:2

justify 22:8 63:5

63:6 106:10,11

315:4 317:7

justin 368:3

K

kaplan 286:3

katz 74:16 75:17

147:15,17

keep 7:1 19:1

27:17,18 28:12

76:1 94:3

102:7 105:5

109:20 113:17

133:1 151:9

162:4 200:1

255:6 282:14

306:6 322:8

323:16 359:11

362:16,19

keeping 18:19

49:10 64:7

316:1 374:20

kelly 6:22 7:16

kept 18:22,22

19:8 22:5

49:10 94:6

336:7 341:7

key 22:20 37:7

48:16 62:18

74:8 83:11

128:22 193:11

208:20 214:1

218:9 252:5

265:12 266:3

kick 206:22

kid 196:1

kind 10:11

13:20 15:17

20:4 22:1 27:6

30:16 46:1

50:22 54:9

57:22 62:9

64:6,14 68:10

68:14,18 72:7

81:11 85:14

86:18 91:13,14

101:13,18,19

108:7 145:1

152:17 159:6

159:13 160:22

165:7,7 176:12

176:15 178:17

187:7 212:9

214:9 261:11

261:12 280:4

325:19 328:12

339:4 346:4

347:1 361:3

362:4 369:8

372:16 373:2

373:20

kinds 45:15 46:2

64:10 73:17

90:21 103:15

161:4,5 219:20

249:3 264:18

280:3,5 308:13

349:11

kinetic 298:12

klatch 77:3

knew 17:21 77:1

77:1 180:4

191:10 202:17

356:6

knocked 120:13

know 13:14 23:7

23:11 24:19

28:6,6 43:2

45:2 50:1,14

56:21 60:15

62:9 67:11

69:21 70:4,13

82:12 83:6

87:14,20 88:2

88:12 93:12

95:1,11 101:18

103:6,7,19

104:14,16

105:15 106:2

108:5,10 120:8

120:15 121:4

122:13 138:7

139:17 142:21

143:17 144:6

147:16 148:7

148:17,20,21

149:1,13 152:3

152:7,11

153:12,19

154:15,17

155:22 157:5

157:13,14,19

158:3,10

160:11 161:11

163:11 168:9

170:1,2,3

172:5,6 175:4

176:8,18 180:8

182:3,21

183:21 184:21

189:22,22

190:16 191:19

193:13 196:1

197:9,16,18

201:7,14 209:5

213:18,20

214:17 215:5

217:12 218:4

220:18 222:17

223:22 240:22

241:18,20,22

242:8 247:7

248:14 249:5

251:6,7,9,18

254:14 255:16

256:16 257:12

257:15 259:18

260:1,17

261:10,15

262:16 263:6

263:19 267:11

268:5 269:3

270:6 271:7

275:18,18

276:17 277:19

278:14 279:18

280:16 281:11

281:18 282:4

286:5 289:21
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

34

290:2 291:2,6

292:5 293:3,17

296:22 304:1

306:18,19

309:19,20,21

312:1 314:18

320:15 322:5,8

322:20 325:10

329:14,18

330:14 332:18

332:20 337:15

339:18 342:17

344:2 345:4,14

348:14 350:14

351:2 352:6,7

352:19,20

353:13 354:9

355:16 360:14

361:2,7,13

364:13 367:16

368:1 369:8

371:13,19

374:11 376:4,5

377:19 378:4

378:16,18

379:6 380:11

381:19 382:22

knowing 58:19

227:6 241:16

251:7 272:22

338:6

knowledge

47:19 50:18,19

105:21 176:6

288:9

known 36:3

76:6 138:2

166:9

knows 77:11

156:19 306:21

337:10 380:20

L

label 152:12

labs 144:4

lack 48:11 56:3

127:16,20

193:8 253:3

291:4 310:3

344:8

lacked 122:3

lacking 327:11

lacks 179:17

248:15

lag 121:18 122:5

laid 46:11 128:4

landscape

227:21

language 222:18

249:6 375:11

languages

158:18

languish 359:14

lapd 182:7

large 37:11 40:2

40:17 50:17

58:20 62:1

66:20 67:5,6

117:17 125:10

171:19 178:4

211:10 246:6

248:2 302:18

333:10 371:13

largely 235:1

289:10 307:9

315:8 380:2

larger 61:2,6,6

83:20 249:19

330:3

lastly 140:22

191:11 333:4

latanya 195:12

latent 117:19

latest 40:9 112:3

latitude 51:13

laud 255:16

299:10

law 2:15 3:18

8:19 18:3,4

22:8 26:18

42:12 51:10

65:9 69:10

70:16 72:6

97:12 112:6,18

117:5,6,8

124:22 131:6

132:10,22

133:20 145:3

145:11 146:11

147:4 168:4

169:1 177:10

177:14 189:16

191:6 217:3

219:17 220:18

221:7 223:4

224:14 243:20

247:18 248:1

267:15 272:16

282:2 287:3

292:5 298:22

304:6 309:20

311:14,21

315:1,5,11,18

327:8 341:8

343:6 352:6

355:1,18,19

356:5,8 364:6

364:8,12 365:1

369:14

lawabiding

133:10

lawful 189:6

260:15,16

261:21 262:16

265:6

lawfully 209:1

laws 9:6 24:10

48:17 103:19

129:14 144:20

145:7 146:8,14

168:22 178:2

216:4 230:16

230:18 301:11

302:2 321:4

341:7 343:15

lawsuits 129:10

lawyer 80:3

135:1,2 247:22

305:8

lawyers 165:2

187:12 249:6

305:11 309:19

311:13,15

354:14 378:3

layer 192:12

layering 82:2

layers 209:9

212:22 223:21

lazy 187:8

lead 38:2 47:19

164:2 217:7

328:20 330:7

331:21 335:15

leadership

238:13

leading 110:16

206:4

leads 47:13

206:10

leagues 197:5

leak 115:9

leaked 49:13

leaking 115:3

leaks 198:12

leaning 33:1

leaping 101:4

learn 47:8

147:22 149:16

190:22 191:18

197:21 311:13

311:14 334:22

341:22 342:8

learned 3:17 6:6

10:14 92:3

230:10 244:9

285:5 304:19

learning 35:10

191:1,8 205:14

251:20

leave 156:8

186:15 187:8

187:12 224:2

319:13

leaves 277:4

leaving 256:4

led 274:5 290:2

lee 3:13 215:10

215:17 216:7

239:17 245:22

263:11 267:13

277:16 281:9

left 4:22 9:20

187:18 286:6

347:6 370:2

378:19

leftover 259:8

leg 345:9

legal 13:19 21:9

41:3,7 42:7

63:16 80:3,9

98:14 129:11

132:16 150:4

174:3,5 191:7

216:3 230:4

238:16,17

247:18 272:18

292:4 293:7,14

293:19 316:18

334:14 348:6

348:12 354:11

355:2 377:7

legality 187:13

legally 8:6 68:3

243:2

legislation

303:19 378:19

legislative 212:7

legislators 8:21

legislature

192:21 193:4
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

35

legitimate 9:7

86:12 147:7

231:10 292:22

legitimately

361:21

length 286:22

lens 33:15

216:22 223:19

340:11

lenses 263:7

370:5

lesbian 122:19

123:13

lessons 3:17 6:6

36:16 58:4

92:5 344:4

letters 129:18

letting 160:2

370:17

level 21:19

23:14 28:22

29:12 30:8

86:14 93:14,19

108:13 127:9

144:9 163:10

164:12,13

175:2 183:12

183:14 184:22

185:21 192:8

193:13 210:10

213:2 253:9

257:17 270:8

273:21 305:15

306:1,10

307:16 309:16

310:14 342:19

343:3 377:21

leverage 249:13

250:4 251:14

levied 227:8

lgbt 122:2 123:4

liberal 235:12

liberties 1:3 4:3

111:4 116:10

203:5 205:10

206:5,11

207:16 209:2

209:13,19

211:3 215:11

215:14,18,21

216:1 217:17

223:17,19

225:12,16,18

226:6,9 227:2

227:18 229:5

229:14 231:1,3

231:13,14,18

231:22 232:4

232:18,19

233:5,10 234:5

234:16 237:6,7

237:12 238:10

238:12 240:3

242:15,22

243:8,19

244:10,13,18

275:7 280:22

282:22 284:13

289:20 295:17

295:21 296:3

303:10 304:18

305:5 310:22

317:10 334:1

349:13 351:18

353:8 356:21

357:7 358:21

383:20

liberty 10:7 59:5

59:10 135:15

317:12

life 17:20 126:13

324:10,11

lifetimes 341:5,6

lift 170:11,19

light 5:9 92:2

178:11 207:4

220:9 243:21

lights 22:14

likehealth

235:22

likelihood 31:7

likeness 5:10

likes 140:19

limit 40:14,16

68:6 90:9

106:6 112:15

124:6 143:3,4

143:22 178:10

178:17 293:15

293:19 299:1

336:21 351:3

351:11 372:10

limitation 329:7

350:13 371:9

372:6

limitations 28:1

226:12 297:7

297:14 312:9

312:15 327:15

342:7 350:8

360:3,5 362:13

362:20 372:21

375:21 379:18

382:17

limited 77:1

118:6 120:2

182:5 271:20

306:19 319:8

343:2 352:22

373:7 376:5

limiting 39:2

54:4 142:15

256:3 299:7

300:14 301:3

limits 22:3

38:14,17,19,21

38:22 141:18

292:17 330:16

351:20

line 44:21 67:19

75:7,20 108:3

120:8 159:17

256:15 259:14

326:1 341:15

347:18

lines 132:10

162:16 173:7

270:7

link 15:18 34:12

39:11,19 161:4

linked 91:20

linkedin 137:1

141:11 339:1

linking 49:1

90:21

links 35:3 90:5

90:22

list 10:18 83:14

83:16 84:19

120:14 288:12

listed 51:6,7

299:15

listen 166:10

listing 311:9

lists 55:18

literally 119:11

353:6

literature 40:17

little 45:14

47:15 53:21

54:19 57:18

60:2 83:1

92:18 110:7,10

114:21 146:1

159:3 162:19

165:19 179:19

190:21 199:20

205:20 217:9

218:18 220:9

227:1 229:13

233:16 248:10

252:2 262:15

270:17 271:7

277:4 288:8

305:14,18

323:22 330:12

347:5,14

live 20:4 158:12

191:15 205:14

245:17

livelihood 59:11

lives 12:4 34:6

49:5 151:16

379:12

living 126:5

283:14 356:22

livingston 1:22

385:4,16

liza 2:13 10:6

41:22 70:8

74:15 103:22

108:2

loaded 255:12

local 220:19

location 114:1

148:3 168:7

232:6

locations 39:16

lock 194:20

locked 306:15

306:15

locks 113:16

log 306:20

310:12

logging 308:10

320:20

logistically

285:22

logs 320:7,17,18

321:1,6

long 22:4,7 32:9

55:22 62:2

94:3 118:2

123:9 126:2

138:16 202:6

229:6 284:2

300:18 309:7

319:16 362:13

362:16

longer 40:6 56:2
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

36

118:8 138:15

158:14 191:10

282:19 289:1

362:19

longestablished

118:5

longstanding

332:12

longterm 59:6

look 11:16 15:21

22:19 26:8

30:14 41:13

43:14 48:7,8

50:3,13 52:13

52:15 59:16

61:18,20 66:18

75:3,10 76:9

76:11 78:6

82:17 87:17,20

87:22 88:4

91:9 95:15

104:16 105:6

116:22 120:10

120:16 141:15

156:13 157:7

170:4,5,5,6

177:1 178:15

178:16 197:2,3

198:22 199:1,3

215:3,6 223:10

223:18 228:8

233:15 236:22

240:20 243:18

250:21 252:2

256:11 257:15

258:1 261:13

261:19 269:7

269:19 270:17

272:2 273:17

275:3 276:15

276:16 310:12

311:5 320:22

321:6 322:3,7

329:19 338:22

339:2 340:17

340:22 347:13

363:19 364:2

366:16 368:14

371:5,12 373:3

373:21,22

382:9

looked 50:15

160:16 281:13

looking 68:11

89:6 90:21

105:6 141:1

142:10 196:19

197:11 220:2

236:19 240:17

240:18 252:9

263:6,18 269:2

269:22 270:15

282:8 288:18

320:12 321:3

339:6 341:13

343:10 357:19

366:6 367:3,4

368:9 371:18

372:2

looks 15:16,22

73:3 236:16

loop 183:20

loophole 300:12

loosen 327:6

loosened 378:6

los 182:7

lose 97:14

257:10 268:18

losing 95:13

148:13 270:11

loss 16:5

lost 16:4 24:10

326:5

lot 21:12,13 25:2

43:11 47:4,6

48:12,17 49:12

49:17 71:18,18

71:20,21 72:5

83:6,18 86:9

88:18 94:11

95:3 96:11,18

120:9 123:8

138:12,14,17

139:5,14,16

143:8 144:19

144:20 145:3

145:10,17

147:4,6,7

154:9 157:5

176:18 183:1

187:2 191:12

191:16 192:22

193:14,19

197:13 212:1

245:10 247:1

251:19 252:12

262:19 264:15

266:2,5 268:13

269:8,9 273:5

273:7 275:5,9

278:7 283:13

284:6 290:6

291:13 292:1

304:1 305:9

307:15 308:3,9

309:3,13

317:16 320:8

328:4 333:1

342:10,19

343:4,7 344:6

344:13 348:8

349:18,19

368:2 370:1

375:20 383:11

383:16

lotion 35:9 37:6

lots 86:1 103:19

220:2 223:21

loudly 154:4

love 189:12

low 305:17

lowly 357:11

loyalty 37:5

lunch 7:4

lynn 7:15

286:15

lynne 1:22 385:4

385:16

M

m 1:14 4:5

machine 35:10

machines 349:2

magicmarker

307:12

mail 107:14,17

main 36:16

184:5 242:5

305:10 371:8

mainstream

10:22 229:9

maintain 191:2

191:3 208:11

214:11 370:16

maintained 57:6

226:22

maintaining

140:9 190:3

214:17,17

maintains

265:12

major 241:20

262:1 298:14

298:21 330:19

majority 53:15

64:20 253:12

makers 349:12

making 7:17

18:21,22 32:19

35:15 100:18

140:13 142:2

148:17 154:7

154:14 167:9

199:5 246:13

249:8 265:11

379:21

malware 189:1

manage 139:19

140:4 237:2

307:18 308:6

310:4,13

342:18 350:2

management

127:3 234:11

248:21 290:18

290:19,22

291:3,13,21,22

304:4 305:19

307:15,16

381:16 382:1

managing

124:19 306:2

310:7

mandate 5:19

231:5

mandating

165:22

mandatory

172:12

manipulate

228:20

manipulated

336:15 378:11

manner 103:18

298:4 378:8

manners 21:17

manuel 217:22

map 343:15

mapping 155:9

marcos 250:19

marginal 73:16

marked 222:2

markers 242:3

market 24:5

42:21 63:16

marketing

127:6 172:16

320:4

marriott 1:13

4:7 206:17
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

37

247:21 264:8

marriotts

206:19,20

marshall 18:2

marvelous 26:2

maryland 385:5

mass 60:16

301:7 310:15

317:2 365:17

masses 43:17

massive 195:2,5

289:13 320:6

massively

335:20

match 85:9 91:8

matching 12:19

material 324:15

math 235:11

270:1 283:13

283:15 284:6

mathematically

113:9

mathematics

269:14

matter 12:17

80:4,5,8 90:21

98:12 149:2

208:7 242:1

253:22 294:18

315:9 338:4

358:11 379:11

matters 5:2

118:21 124:5

215:14 338:5

mature 126:21

maximize 61:17

mean 9:13 11:7

13:9 23:9,10

30:13 44:11

46:21,22 52:7

61:14 64:5

69:12,17 78:6

78:16 84:18

87:10 102:19

103:13 121:20

144:9 149:20

151:5,8 157:17

160:7 161:20

168:3,22 169:3

180:17 184:22

188:1,7,17,18

190:16 191:17

195:11 202:10

210:18 214:20

232:9 244:19

248:14 273:4

277:3 282:1

289:10 290:20

291:3 321:11

327:18 332:15

336:20 340:9

340:10 353:6

354:13,18

355:20 356:1

367:10 368:17

369:19 371:3,8

371:21 377:11

377:22

meaning 138:3

248:15 277:21

326:3

meaningful

145:19 190:19

232:16 247:4

263:18 282:2

290:10 299:9

301:1 329:21

331:11

meaningfully

333:2

meaningless

328:12

means 11:20

13:4 20:14

28:22 72:14

79:3 124:1,2,4

187:16 189:4

202:3 221:17

237:4 252:1

265:20 268:1

286:7 289:6

295:6 299:7

332:21 339:10

339:18 352:22

355:12 356:2,2

356:3 360:6

meant 180:5

334:2 352:9

measure 119:8

123:22 141:6

221:11,18

345:18

measured

291:18,18

measures 23:21

23:22 68:5

112:8 163:22

213:22 225:19

276:3 281:5

mechanicism

31:1

mechanism 33:4

89:22 178:16

212:10 263:15

263:22 319:18

322:16 346:17

mechanisms

29:19 30:12

31:6 55:10

56:8 57:7,13

63:17 86:1

93:1,8,20 95:2

101:15 173:22

210:22 211:19

212:4,16

244:22 306:4

308:8 316:3,4

319:1,7,15,19

321:1 335:13

342:5,15

media 338:14,22

340:3

medical 98:21

99:5

medicine 304:9

medieval 76:21

medine 2:3 4:2

4:20 74:14

79:21 80:4,6

80:13 107:2

108:19,22

109:5 116:14

116:18 165:14

165:15 166:18

167:18 168:16

169:8 171:9

194:6,14,18

196:7 203:4

264:2 268:9

284:17,22

354:6 355:7

356:11 357:22

360:10 363:1

383:3 384:13

meet 39:4 238:1

314:11

meeting 4:4,6

5:17 116:12

117:1 193:15

193:18 203:6

295:22 347:4

meetings 224:11

253:20

meg 139:10

mellon 251:8

member 6:10,11

41:18 102:6

110:11 134:14

141:20 192:2

203:8 226:8

255:2

members 2:1

4:12,13 6:11

6:13 7:9 9:18

9:21 10:12

25:10 26:13,14

32:5 41:17,19

74:12 101:6

109:9 110:4,5

110:6 122:19

123:4,13

141:17,21

154:12 159:15

181:9 185:7,8

252:16 254:8

254:18 256:1

278:5 286:10

286:14 295:20

347:20 352:13

378:21 383:6

383:21 384:3

mention 147:1

210:6 220:14

290:3

mentioned 70:9

99:22 115:21

125:1 142:22

144:21 153:7

172:19 177:8

182:5 190:17

193:6 212:20

219:16,22

221:16 223:2,9

239:21 243:14

246:1 249:22

250:1 280:21

293:13 319:10

mentioning

223:20

mentions 89:1

mere 78:14

170:6

merely 232:21

371:18

merge 34:2

merged 34:17

34:19 36:21,22

merges 34:13

merging 34:18

34:21 35:2
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

38

37:3,16

merit 362:2

380:12

meritorious

362:17 365:20

merits 113:5

mess 8:7

messages 136:8

messy 117:17

metadata 37:10

102:12 103:4

152:1,4,11,12

190:18,18

metaphor 260:7

method 106:12

160:8 218:11

267:11

methodologies

237:5

methodology

59:4

methods 35:5,10

40:18 62:16

89:8 91:21

92:6 182:21

214:18 218:16

219:3 267:2

metric 165:7

mic 318:16

323:21

michael 3:6

190:16

micro 28:22

microphone

203:15,18

375:15

microsoft 3:6

124:12 125:2,3

144:14 149:7

149:10 150:17

150:17,20,22

151:1,2

mics 318:17

middle 92:18

346:3

midst 316:14

mike 124:11

134:8 144:13

149:5 167:19

185:21

mikes 164:20

military 122:21

123:2 227:16

228:4

million 361:1

mimic 353:3

mind 56:5 82:20

101:13 121:8

145:13,16

180:22 201:4

306:6 318:15

336:7 346:9

366:12

mindful 146:14

182:4

minds 148:15

mine 199:15

259:7 271:8

miniminization

272:12

minimization

118:7 126:1

221:16 233:8

297:7,16 300:4

300:5,6 301:18

327:14 328:1

329:9 358:9,14

359:3,4 372:4

376:2

minimize 106:5

204:20 361:13

382:10

minimized

358:3,5

minimizing

62:14

minimum 62:20

mining 47:9

181:17,21

minister 166:12

minorities 122:2

minute 109:2

110:2 140:3,3

159:16 201:9

217:11 284:18

353:18

minutes 9:10,12

9:16,17 41:16

74:11 76:1

109:21 139:7

141:19,20

205:20 217:7

278:10 286:1,5

286:9,10

misbehavior

27:21

misconception

119:3

misconduct

31:15 192:15

misinterpreted

22:16

mismatch

351:13

mismatched

346:18

misplaced 13:18

missed 173:5

184:12

mission 216:20

227:5,22

298:13 304:13

383:18

missions 215:15

298:7

mistake 55:19

93:16 316:15

mistakes 173:11

174:17 213:9

misuse 56:21

86:6 94:18

misused 55:17

267:4

misuses 266:22

mit 251:8

mitigate 46:15

92:20 225:2

232:17 261:8

262:2

mitigated 20:21

66:9

mitigating 93:1

93:4 280:17

mitigation 92:19

mobile 135:21

136:4

mode 58:12

model 35:21,22

83:5 115:12

118:14,19

121:9,11,13

135:10 155:7

155:19 157:22

modeling

111:14 114:15

114:19 115:19

182:9 183:3

199:20

models 114:19

182:19,22

183:2 261:11

261:12

moderate 193:7

moderated 6:9

7:19 203:8

moderating

109:6 285:8

moderator 6:17

9:16 286:8

modern 32:11

97:21 186:4

329:11

modest 58:19,21

91:4

modifications

85:14 374:12

modified 88:3

modify 212:4

modifying 85:5

moment 316:2,2

323:20 324:1

325:10 347:11

347:11 362:6

369:18 380:17

momentum

178:14

monday 224:18

money 342:20

monitor 122:16

monitoring

343:21

monitors 22:12

months 95:14

311:20 379:11

moot 78:14

moral 121:18

122:5

morality 27:18

morning 4:2

18:5,6 109:9

109:10 110:19

117:9 184:17

202:22 203:13

203:19 204:7

234:1 245:11

255:21 268:12

285:13 287:14

mortal 313:20

mosaic 36:13,18

47:11 75:13

76:7 77:9 78:5

80:11 89:1,2

89:10,12,18

97:5 383:14

mosiac 268:13

mother 13:8,12

motivated 99:12

motivations

44:9

motive 44:17
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

39

mount 141:5

170:13,20

move 19:10 96:6

116:6 236:16

270:9 301:6

383:18 384:10

movements 15:2

148:8

movie 160:17

moving 89:21

94:15 96:15

219:7 305:16

318:15 369:10

370:13

multifaceted

173:14

multihop 90:22

multilaterally

153:13

multilayered

173:15 358:20

multinational

343:13

multiple 308:20

308:20 360:19

370:5

multivaried

32:15

music 135:8

muslim 16:18

muster 332:4

mutually 155:21

296:22

mysterious

369:9

mythical 331:7

N

n 1:13

nahari 3:7

134:15,20

154:17 155:1

158:11 169:22

182:3 189:9

201:13 202:15

nail 150:12

naive 166:17

naivete 355:15

name 5:10

237:13 306:20

names 175:13

194:9

nara 222:5

narrow 18:15

19:10 53:15

65:4 72:3

365:12

narrower 360:1

narrowing

300:14

narrowly 31:5

45:18 366:1

nation 23:4

111:2 115:2

116:13 189:19

209:3 226:11

226:21 227:11

228:4,6,9

234:20 302:2

national 3:11,14

8:17 10:8 46:2

53:22 64:6

66:13,18,20

67:4,5,6,9

69:10 81:1,12

84:13 85:9

89:9,13 104:10

111:15 112:18

123:22 129:14

129:18 133:20

162:16 176:13

181:15 200:11

204:5 206:6,12

218:15 219:19

222:6 223:2,3

223:4 225:11

225:22 226:15

246:19 256:21

257:1,10 280:1

285:7 294:10

296:3 297:20

297:22 298:8

298:18 299:8

302:5,7 310:19

327:11 330:19

331:3,12

335:11 367:9

375:19 381:21

383:19

nationality

231:9

nations 111:22

112:19 325:20

365:18

natural 26:18

106:15

naturally

378:21

nature 32:15

58:11,22

188:19 226:14

229:1 230:19

333:18 336:1

379:6

naval 313:10

navigate 335:17

near 308:5

nearly 124:21

necessarily

24:15 98:12

155:4 168:21

182:8 184:8

190:9,15

218:20 275:15

279:2 284:7

305:9 315:4

333:13 338:11

365:4 376:8

necessary 31:7

40:20 55:3

57:22 161:13

178:11 192:8

208:21 259:17

290:11 300:18

301:22 317:8

345:5,6 370:16

necessities 86:7

necessity 54:17

204:11 208:9

317:6 345:3

362:14 366:3

necesssarily

218:21

need 23:11

24:20 26:6

28:22 29:6

32:1 46:2 47:1

47:21 52:13,15

53:14,16 62:10

63:4 66:7 67:1

67:4 68:16

83:10 89:11

90:3,4 93:17

93:20 94:3,10

105:4 113:22

119:18 127:21

130:10 132:8

133:6 134:3

143:15 146:13

146:19 155:4,7

168:12 170:9

170:17 172:7

174:18 175:2

176:12 183:1

187:11 190:9

193:16 194:4

198:8 200:13

210:16 214:8

214:21 234:17

245:5 254:18

257:4 261:21

265:2 267:4

270:19,19

283:15,16

294:11 309:21

310:3,8,9

312:5,7,9,12

312:13 316:5,6

317:13 319:6

319:13 320:13

320:14 321:17

331:5 345:15

347:7 348:2,3

348:5,5 349:5

349:12,15

354:7 355:17

355:21 359:12

360:8,9 373:2

377:18

needed 40:21

118:14 217:14

243:18 282:21

300:15,20

307:4 359:7

needing 95:22

needle 257:5,7

needs 39:4

54:16 66:2,3

67:15 85:9

90:17 116:13

128:2 133:8

134:1 167:17

179:18 193:1

195:9 204:5

236:17 254:2

259:1,10

273:20 281:6

311:3 374:8

nefarious 112:2

negative 20:10

35:19 175:7

204:20 291:8

291:17 312:22

381:17 382:8

negatives 56:12

negligent 95:10

neighborhood

183:12 184:20

neighbors

148:22
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

40

neither 44:17

nervous 80:8

355:8

netflix 160:15

161:10 195:11

network 197:16

199:7 369:16

369:16

networks

131:10 197:22

neutral 374:2,16

380:4

never 55:16,16

55:18,19

120:16 164:1

303:17 326:5

351:7 354:1

nevertheless

131:11

new 25:15 47:8

50:18,18 76:14

78:18 118:14

118:19 132:16

132:17 140:9

143:4 145:18

157:14 158:15

172:2,3 177:8

190:9 191:9,11

209:20 225:13

230:4 231:4

241:9,9,10

250:1 260:13

260:13 271:22

273:2 330:18

364:9

newer 340:5

newly 209:12,18

news 16:17

163:19

nice 225:9

235:12 325:8

nodding 97:10

98:2 107:18

nofly 83:14

84:19

noncontent

152:1

nonexistent

307:6

nonintelligence

231:17

nonpregnant

35:21

nonstate 228:7

nonstatutory

300:9

nontechnologist

137:7

nonu 132:9

282:9 301:8

302:11

noon 7:5

normal 15:4

78:9

normative 167:4

notable 234:8

notarial 385:12

notary 385:4,17

note 6:21 28:14

75:22 78:3

116:1 179:13

noted 81:2

358:1

notes 61:9

nothings 173:13

notice 83:8 84:2

84:17 272:5

289:11 290:6

290:10 312:14

328:6 329:5

332:13,19,21

343:4

noticed 203:13

326:12

notified 84:6

notion 8:2 55:4

69:7 80:17,20

81:11 82:3

89:21 99:1

155:16 156:10

167:11 190:17

201:17 289:15

363:6 370:20

374:1

notions 196:20

197:6

notwithstandi...

369:8

novelist 8:3

november 1:7

4:5

novo 53:6

nowadays 40:7

nsa 3:20 13:11

81:4 82:5

84:11 103:8

104:7 130:20

164:3 165:3

172:2,4 177:3

177:9 225:15

227:14,22

228:1,8,10

230:3,10,14

231:20 232:4

233:3,6 234:2

238:10,13

248:17 250:14

251:1,17,22,22

269:13 270:16

275:12 276:8

283:13 284:1,6

301:13,15

313:6 314:14

316:13 319:14

320:8 323:1

325:16 330:11

333:21 351:6

351:15 357:4

360:16,21

364:2,13,19

365:9,14

380:16

nsas 15:14 23:14

225:17,20

226:6 227:2,5

227:18 229:14

229:17,20

231:1,15

314:20 351:18

nuance 29:1

348:21

nuanced 307:20

368:5

nuclear 60:4

227:12

nudge 39:2

number 17:13

37:5 66:4

141:6 149:19

153:21 164:5,7

164:9 172:8,9

172:11 173:6

189:19 198:13

200:17 287:4

320:19 363:4

numbers 91:12

91:19 175:15

175:15 194:9

229:10

numerous 88:22

nuts 96:1 354:12

nvidia 3:7

134:16

nypds 16:18

O

oath 244:4

obama 132:7

obfuscate 68:7

objected 339:5

objection

339:14

objective 86:5

219:2 223:12

objectives 27:5

obligated 309:9

obligation 86:20

240:13

obligations 38:9

obscure 14:20

15:5

obscurity 5:3

15:8 74:19

76:19,22 77:5

observation

15:4 27:14

30:1 78:9

observations

176:10 257:19

287:22

observe 293:20

observed 5:5

79:10 337:15

obtain 81:20

131:2 221:8

227:5

obtained 223:11

266:19 280:11

352:4

obtaining 261:4

262:19 265:8

266:6

obvious 11:19

39:22 42:14

348:11

obviously 28:2

43:5 63:16

81:7 89:2 95:3

99:17,18

102:20 150:13

154:15 163:7

176:9 194:14

194:21 204:2

221:6 223:18

241:15 272:7

272:16 273:9

276:20 297:5

311:8 341:22

342:11 350:11

367:11,12
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

41

occasion 148:5

occur 37:3

occurred 228:21

occurring

185:18 187:22

240:16

occurs 110:8

184:1

october 4:10

odd 294:11

odds 69:7

315:12

odni 239:1

244:20,21

249:15

odnis 206:10

oecd 329:4

offensive 111:1

offer 32:14

33:18 38:14

63:11 86:22

257:19 287:21

offers 39:21

291:13 357:15

office 3:11 206:6

206:11 215:16

215:22 238:3,4

238:5,7,10

240:1,6 243:14

243:15 244:6,7

245:4 246:5,19

351:18 354:20

officer 4:11

120:12 171:18

172:3,4 206:5

206:21 215:11

223:18 225:13

226:7 239:12

officers 56:20

203:2 243:7

264:18 265:10

349:12,14

offices 209:11

209:11,13

223:8 244:5,8

244:11 264:5

358:22

official 224:10

239:22

officials 3:10 6:4

8:21 15:13

23:7 66:5

204:1 205:5

215:21 232:10

383:9

oftentimes 48:1

218:21 358:5

oh 136:9,11

187:8 189:11

199:6 201:11

207:3 262:6

305:2 355:3

ohm 195:12

ohms 311:11

okay 18:1 33:8

41:15 42:13

45:10 52:4

67:17,21 82:21

83:3 87:7

88:11,13 102:1

102:2 103:21

105:9 106:14

106:17 108:16

134:11 159:14

161:8,9 163:1

164:14 167:18

179:22 180:16

194:14 201:4,8

207:7 255:8

257:18 259:6

267:13 274:7

275:20 277:7,8

305:2 354:14

357:15 369:14

369:20 383:1

old 106:21

117:20 137:22

247:12 259:7

364:8

older 322:10

omg 354:8

357:20 358:19

359:21

once 29:16

157:19 186:3

191:1 258:22

293:8 300:8

321:1 351:1,2

ones 5:2,6,10

13:4 42:2,10

51:11 57:17

83:19 85:8

94:20 163:7

210:3 252:17

272:10 273:17

279:16

ongoing 142:19

263:16,16,22

online 16:10

34:9,10 39:12

39:19 69:1

125:8 151:10

152:9,13

160:16 161:10

onset 177:17

ontological

26:15

onus 63:5

open 54:16 55:3

107:14,16

219:10 286:11

318:6

opening 4:17

109:15,20

157:22 172:6

237:21 340:10

openly 122:20

123:12

operate 95:22

126:10,18

208:9 346:8

operated 204:19

228:2,11,14

operates 130:4

operating

210:21

operation

204:12 374:14

operational

28:20 32:2

85:9 86:7

218:19 315:18

operations

215:15 217:20

217:22 225:9

227:16 313:11

355:17 373:9

operators 239:6

316:18 334:15

334:16

opine 253:6

opinion 80:3

148:6 199:12

368:19 369:9

375:2 380:5

opinions 301:22

374:10

opportunities

91:8 226:19

opportunity

6:12 25:11

33:17 96:5

110:20 117:10

124:16 134:20

230:22 237:14

237:18

opposed 43:22

44:19 54:8

70:22 192:19

251:6 263:19

277:9 287:22

318:5 338:8

355:22

opposite 11:21

214:14 243:10

355:20

opposition

69:18

option 197:18

328:10

options 39:22

90:8,12 308:3

308:4,6

oral 120:3

318:21 326:18

order 4:16

68:10 70:12

90:5,18 109:18

130:4 159:17

196:13 208:12

217:13 221:8

221:14 229:18

238:14 254:2

254:18 257:5

259:1 265:2

279:1 301:5

323:9 324:14

orders 128:9,9

128:20 129:18

129:22 169:3

301:22 325:18

ordinary 128:13

228:17

organic 326:3

organization

115:13 234:3

244:21 252:4

264:21 265:7

268:3 308:12

320:8 357:17

357:19

organizations

34:7 38:7

106:19 107:6

232:2 264:19

304:13 310:8

320:16

organizing 63:8

orient 217:1

orientation
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

42

216:15

oriented 114:18

originally

175:13

ought 42:11

63:15 75:18

229:7 344:17

359:6

outcome 385:11

outcomes

291:10

outcry 298:15

outdated 25:21

outlined 101:21

outputs 143:14

outset 332:14

outside 10:21

13:17 29:18

31:4 43:5

57:20 59:4

94:16 95:4

116:19 194:1

209:13 230:14

353:7 355:13

369:18 373:3

373:20,21,21

overall 112:19

114:5 200:8

261:13 262:3

263:2 301:6

overarching

23:3 49:21

overcome 68:20

overlapping

296:22

overlook 331:13

375:7

overlooked

173:5

overly 353:11

overrepresent...

184:2

override 65:2

overriding 12:6

oversaw 303:13

overseas 229:22

oversee 41:4

254:18

oversees 215:17

oversight 1:3

4:3 21:19

24:12,17 30:11

31:19 38:4,22

41:7 42:7

52:12,16 71:12

72:19 73:2,18

73:19 94:11

95:21 96:5

130:9 157:2

165:10 169:4

174:17 177:5

178:1 179:15

192:4,6,14,18

193:2 194:5

203:5 207:13

209:6,7,17,19

210:1,6,7,10

210:12,14

211:1,18

212:16 219:15

219:16,20

223:2 225:17

238:22 239:19

241:17 244:8

246:1,3,17,18

247:4 252:18

253:2,10,14,15

253:21 254:3,6

254:22 276:16

276:19,21

277:17,21

295:21 306:4

307:5,15 308:8

318:19 319:5,7

319:9,12,15,18

319:22 321:12

321:14,19

322:16 324:21

338:1 342:21

349:12,13

375:18 376:2

383:13

oversights 30:10

56:8

overtime 327:12

overusing

190:17

overweighing

73:13

overwhelming

144:17 319:4

ownership 70:3

P

pace 341:8

page 241:10

pages 119:13

163:1 220:8

paint 36:15

pairing 173:22

paladin 3:20

313:5

palantir 303:10

304:2,4,11,20

305:19 309:9

309:11 311:10

320:15 338:19

349:4 367:8

palm 136:11

panel 6:6,9,19

7:18,20 10:2

26:14 31:14

32:5 41:17

69:22 85:21

88:16,18 96:10

102:6 108:17

108:22 109:3,7

109:10,11

110:8,12 119:3

134:14 135:6

142:22 155:3

164:21 171:11

171:13 179:9

181:10 185:10

199:10 200:5

202:22 203:1

203:21 204:22

255:21 256:1

269:11 276:20

278:5,10,17

285:3,4,13,16

285:17,17,18

287:14 288:3

288:18 310:5

319:11 333:12

333:13 341:17

347:20 363:6

363:22 383:4

panelist 9:9

165:16 252:22

286:1 295:14

303:9 313:4

panelists 6:17

6:18 7:1 9:3

10:4 25:19

64:17 79:7

88:7 105:11

154:13 185:10

203:7 206:3

275:21 285:10

286:2 313:12

318:10,14

384:3

panels 5:20

116:5 140:7

178:12 179:9

199:10 206:16

286:13 313:18

383:4

paper 271:13

276:18 306:13

319:1

papers 12:21,22

42:3

paperwork

192:12

paradoxically

361:15

paralyze 379:3

pardon 119:19

parentage

303:20

parents 303:21

parker 7:15

286:15

parking 137:1

part 11:20 49:9

55:11 65:16

67:13 82:10

83:22 86:3

87:10 107:22

108:17 120:10

137:11 155:9

172:1 178:15

207:15 210:14

212:8,15,19

214:1 215:16

216:20 224:22

225:4 227:5

232:19 233:6

240:15 241:2

243:18 244:4

247:8 250:5,12

262:20 274:20

277:3 280:22

289:8 291:5

298:12 320:13

339:2 346:4

372:5,7

partial 71:6

participate

10:13 124:17

299:6

participated

360:17

participates

362:18

participation

19:3 80:22

82:3,9,16 86:3
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

43

163:8 221:5

271:18 272:8

274:9 326:15

326:22 327:10

327:19 328:14

329:9,18

particular 9:8

21:15 29:2

37:8 40:19,22

41:18 49:2

65:10 66:21

67:2,2 86:17

94:1 102:6

121:22 129:5

181:1 192:1

204:13 208:4

211:15 214:4,9

216:13 220:14

225:10 240:12

241:3 245:2,19

248:17 249:14

280:9 281:16

286:16 315:14

323:19 328:1

333:20 335:9

particularily

278:9

particularly

47:5 109:10

124:7 151:13

168:2 203:14

210:8 240:22

244:6 245:8

246:3 285:19

289:16 294:10

334:16 342:7

parties 42:17

63:13 91:3

151:18 153:22

375:9 376:21

385:10

parting 108:18

partner 313:5

parts 47:11

94:13,15

230:17 233:12

250:17 273:15

360:13 371:19

372:19

party 150:16

151:6 168:17

361:22,22

pass 187:20

286:19 332:4

364:7

passed 145:8

230:16

pat 255:7

path 276:11

paths 91:9

patricia 2:5 4:15

patriot 103:5

301:12

patternbased

181:16,20

paul 2:14 25:4

64:12 68:15

69:4 98:2

100:18 106:14

195:12 311:11

pauls 40:4

pausing 81:15

pay 243:8 286:6

287:12 336:16

paying 157:4,5

pays 190:7

pbgc 106:20

pclob 110:6

114:9 116:2

159:9 279:18

295:6 303:1,19

318:8 353:6

355:6,10

358:22 361:6

382:21 384:9

penalties 222:22

pension 107:2,3

people 6:15 9:22

10:2,17 11:22

15:20,21 16:4

16:7 17:3,7,8,9

17:13,19 18:9

18:10 19:1,2

20:9 21:5,22

22:6,17 23:5

23:17,20,21

24:3,6,7,7,9,16

34:4,7 35:6

37:1,21 43:2

44:10,13,18

47:19 48:11,17

49:12 54:17

56:1 58:1,16

58:20 59:18

64:2 65:22

68:1,5,8 69:20

70:1 71:5,6,9

74:8,18 76:15

79:3 81:18

83:6 95:8 98:6

99:3 104:2

113:14 120:12

120:18 122:2

123:8 132:1

133:9 146:9

147:21,22

148:5,17,21

149:16,18,20

151:8 152:20

153:8 160:18

163:18 166:22

172:16,17,20

173:7 178:5

180:7 183:15

184:13 197:3,4

197:15,18

198:14,20

199:5,22

200:19 214:7

242:11,20

243:11 247:1

249:11 251:7

252:19 255:20

260:4 262:19

263:5,17

264:18 266:18

267:2,19

278:19 283:13

284:5 294:18

294:20 303:17

304:2 309:21

310:3 312:9,11

319:13 321:15

321:15,17

322:1,4 328:18

329:14 330:15

332:18 338:16

339:10,20

342:22 345:11

346:14 352:4,8

354:22 355:1

357:20 361:1

363:17 368:3

379:17 381:2

peoples 12:4

18:19 21:3,4

23:16,17 42:20

45:5 47:18

53:9 57:2,5

59:10 128:13

148:15 150:9

151:16,18

153:3 198:17

209:2 373:10

379:12

perceived 213:6

315:13 317:19

percent 16:9

39:18 43:3

56:4 70:12

93:17 157:10

157:10 159:13

163:19 196:22

198:16 363:13

379:1

percentage

70:10

perception

253:4

perceptively 8:4

perfect 111:11

114:4,7,12

115:18 133:18

160:2,5,10

161:21 162:3

173:13 194:21

213:8 241:12

perfectly 70:4

100:10 354:11

perform 38:10

104:22 254:3

254:22

performance

134:17

performed

233:11

performing

254:17

period 138:17

196:11 206:2

periodically

241:8

periods 15:2

93:2

permanently

196:16

permissable

238:17

permission

348:10

permit 29:10

299:9

permitted 130:2

222:16 243:2

294:3 314:22

permitting 6:16

perpetrators

183:16

persecute 44:10

persecution
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

44

55:18

person 12:20,21

15:9 34:15

79:12 97:14

98:9 103:6

120:14 150:15

230:2 238:19

240:6 256:13

256:15 269:5

278:22 279:3

289:20 294:16

311:1 376:8

personal 5:1,11

14:10 17:10

20:13 27:18

43:12,21 51:11

118:21 127:8

133:9 136:9

228:18 229:1

231:11 232:3

235:4,14,17

236:7,7,11,13

242:17 254:15

265:8 368:19

personalities

54:18

personality

37:13

personally

85:18 160:21

178:3 229:8

243:5 247:16

249:10

personnel

126:22 127:6

persons 15:1

29:15 66:21

67:2 78:10

113:21 118:22

131:18 132:1

164:5 231:7,10

279:3,21 282:9

301:8 302:11

perspective

33:19 94:15

97:7,8 124:19

135:7 137:12

184:6 208:8

216:17 219:6

220:7 229:14

244:14 251:5

254:15 265:17

266:16 270:12

270:13 273:14

277:17 280:1

280:15 334:10

338:14 360:16

360:21

perspectives

232:9

pertain 315:6

317:4

pertaining

225:16

pervasive 30:22

341:2

pervasiveness

30:19 186:10

pessimistic 8:8

peter 6:14 7:16

ph 110:14

145:19 284:6

phase 78:22

218:7 258:1

346:1,21

phases 37:17,17

255:19

phenomenon

142:10

phishing 339:3

phone 37:10

90:15 91:3,12

91:18 135:21

136:1,4,5

166:8 170:12

194:8,12

278:19 301:14

301:16

phones 112:3

135:22 139:5

363:17

phrase 363:4

366:9

phrasing 260:21

physical 336:21

physically

170:10

physicians

122:22

pick 106:16

297:3 326:20

picking 203:18

picture 36:16

123:7 156:16

156:20 170:18

249:13 261:13

piece 64:8

203:12 222:3

256:13 324:2

371:5

pieces 47:5,15

76:12 263:20

306:13

pierce 15:7

pike 341:1

pilot 249:22

pioneers 205:8

pipeline 34:1,13

35:4

place 42:18 96:2

137:1 144:20

178:5 180:4

209:22 232:3

233:14 242:3

245:19 252:8

267:3 269:5

270:22 272:7

273:11 275:3

275:14 288:22

300:2 323:7

330:10,22

331:7 333:1

335:16,20

345:11 352:5

357:10,10

361:8 364:19

placed 39:17

376:20

places 5:8 29:15

108:8 149:1

305:4 311:12

planning 159:22

platform 220:17

304:5

platonic 49:22

72:2

play 73:21 74:7

74:7,9 83:12

172:17 272:21

373:8 375:18

377:1

players 209:9

playing 73:21

239:1

plays 12:4

315:16

plcob 7:8

pleasantly

242:19

please 4:17

110:18 134:19

154:14 201:12

286:6

plenty 16:4

78:21

pllc 2:14

pockets 139:6

point 15:12 16:2

16:3 18:7

19:12 20:12

22:21 23:19

35:15 39:21

49:21 51:21

57:19 60:9,14

60:17,20 66:12

66:17 69:5

70:7 78:13,14

79:4,8 81:22

91:10,10 93:6

96:19 100:22

101:5 102:12

108:2 120:4

123:15,17

142:2 144:14

146:22 147:15

159:15 162:21

179:12 183:2

189:2,10

192:13 238:11

245:3 246:1

256:19 257:2,9

257:9,22 258:6

260:3 275:15

277:12 286:8

290:8,13 292:1

300:7 305:22

307:17,19,19

308:8 310:14

310:14 315:8

316:22 319:11

323:14 324:13

335:9 350:7,9

354:7 356:15

361:14 367:12

371:8 377:6

pointed 64:12

75:12 195:11

252:18,22

pointing 306:5

points 18:6 71:4

76:18 77:13

118:20 252:12

277:21 307:13

309:4 346:1,21

police 56:20

57:3,5 120:12

183:10 185:2

policies 38:8

83:7 95:20

119:9 125:15
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

45

125:16 129:3

144:20 145:8

214:15 215:4

216:5 230:18

234:14 338:21

348:5,12 367:1

policy 25:8

33:14 41:3,7

80:4,5,8

110:17 113:4

113:12 115:5

142:13 145:2,3

174:13 175:5

240:12 251:4

257:8,14

259:22 282:10

315:5 322:6,11

327:6 329:17

339:3 342:3

349:11 350:4,5

352:21 360:15

362:6,8 380:6

polishing

182:19

political 44:14

45:3 122:3

politically 44:19

327:5

poll 16:8 197:1

363:12

pollution 20:19

pool 46:10

pop 269:19

population 36:4

371:10

portion 250:15

portrait 47:11

pose 6:12,17

41:16,18

159:15 181:14

185:5 255:22

368:16

poses 6:11

position 70:4

169:6 206:21

216:18 238:13

239:18

positions 242:12

264:11 268:1

positive 27:2

35:18 128:1

129:4 229:6

291:10,16

293:22 354:19

positives 56:12

56:14 58:16

positoin 273:8

possess 305:9

possessing

302:15

possession

202:18

possibilities

169:19

possibility 56:17

56:19 87:5

110:4 141:21

142:18 345:15

365:21

possible 7:17

56:10 57:1

118:2,3,15

143:14 156:19

157:1 174:16

235:6,16 236:3

236:6,8 348:18

349:16 378:8

possibly 102:7

186:9 343:1

377:8

post 70:22

130:19 197:1

363:12

postal 106:20

107:13

postchurch

85:13

postcollection

87:14 118:17

121:10

posted 384:8

postindustrial

76:19

posting 191:16

posture 111:22

potential 30:18

115:7 142:14

144:7 169:17

174:15 234:18

265:4 268:20

291:17 348:8

381:17 382:7

potentially

222:20 253:1

370:17

power 21:12

36:18 44:7

122:3 302:14

302:16,21

306:7 337:5,12

337:22 357:17

powerful 117:15

124:5 163:10

167:6 337:13

337:14 357:11

powers 15:4

78:9 111:16

314:7 375:9

378:5

ppd28 231:5

practicable

162:7

practical 12:17

74:18 76:18,22

77:5 92:10

106:5 113:13

113:19 115:16

159:4 171:15

235:7 292:20

358:11,14

practically

14:19 15:5

242:6 373:16

practice 38:1,16

63:15 100:2

106:6 114:10

146:7 204:9

205:8 287:19

296:9 303:3

314:4 315:18

354:9 364:12

practices 33:20

33:22 39:7

62:8 63:6

108:13 111:21

112:12 125:21

193:1 197:22

231:21 296:20

299:11

practicing

267:15

practitioners

316:18

pre 70:22

pre2007 137:22

precedent

150:14

precise 34:19

35:2 168:7

307:11

precisely 99:10

182:13 307:19

precision 34:21

38:15

predicated

219:8,14

221:21 223:9

predication

219:1,10

predict 36:8

37:20 145:20

183:3,21

184:19

prediction

182:11

predictions

37:12 183:13

predictive 35:5

35:17,21,22

182:9 183:3,13

184:9,15

preerror 95:2

preferences

33:5

pregnancy 35:9

pregnant 35:20

37:9

prem 286:16

premise 96:21

96:22 98:16

158:8 186:13

186:14 282:18

283:1,3,7,9,12

313:18

premised 98:22

premises 81:16

prepared 7:7

63:10

preprocessing

62:14

prescribed

362:21

prescriptive

221:18

present 4:12

68:10 227:3

268:16 336:22

presentation

380:18

presented 249:9

president 60:12

128:17 132:7

presidents 23:15

50:14 231:5

presiding 4:11

press 31:9

131:12

pressured 13:15

presuppose

283:19
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

46

pretend 197:8

pretext 44:18

pretty 77:2

90:20 113:17

159:11 162:3

193:3 194:20

195:17 199:4

343:22 381:16

prevent 126:14

131:8 174:17

175:1

prevention 85:3

previosly 229:2

previous 119:10

155:3 171:13

179:9,13

252:22 276:20

278:17 286:13

287:4 319:11

363:6

previously 90:5

117:6,17,20

148:5 228:2,11

303:11

price 336:13,16

pride 303:19

primarily 111:6

119:4 121:9

202:8 229:16

326:17

primary 94:18

256:2 324:19

346:9

prime 166:12

princeton 2:12

33:12,13

principal 94:18

215:12

principally

56:13 59:3

principle 23:3

61:20 62:7,18

86:21 202:14

221:6 254:1

297:8,16 300:4

328:1 332:16

principled

237:5 268:3

principles 61:9

61:10 64:10

84:12 86:17

100:2 134:4,6

164:12 180:1

192:18 204:9

231:21 234:22

272:1,17,20

273:3 274:18

276:10 288:12

296:9,21 303:4

317:3 332:9

princples 220:5

prior 45:14

184:3 199:15

206:14 225:22

264:11

priorities 227:8

priority 200:7

201:15 227:10

prison 104:6

137:1

privacy 1:3,5

2:10 3:2,4,10

3:10 4:3,4,21

5:14,19,21,22

6:4,4 7:22 8:2

8:4,10,13,19

8:22 9:5 10:15

10:16,19,22

11:5,9,15 12:9

12:11,13,15,16

13:3 14:4,7,10

15:10,11,19

16:22 17:6,8

18:7,9,13,14

18:14,15,17,18

18:19 19:9,11

19:13,15,16,22

20:3,6 22:22

24:14 25:2,14

25:20 26:6,7

26:11,15,19

27:3,8,13,20

27:22 28:2,6

28:11,16,17,19

29:3,10,20

32:15,16,20

33:16,21 36:6

36:17 40:13

42:1,5 43:3,19

45:8,13,16,16

46:8,12 47:18

48:1,5,17 49:9

50:1 52:7,15

52:20 53:22

54:9,13 55:2

58:2 59:14

61:5,12,17

65:2 67:7 69:1

69:17,18 72:2

72:11 73:6,8

74:16,17 75:4

75:10,18 79:3

79:4 83:7

86:13 87:21

96:21 97:6,15

98:1 99:2

101:15,20

106:19 107:6

109:11 111:4

111:13 112:19

113:7 114:5,18

114:21 115:15

115:19 116:10

116:12 117:4,7

117:12 118:5

118:12,13,16

118:22 119:5,8

119:18,22

120:12 121:1,9

121:14 124:2,6

124:11,14,19

125:4,4,6,19

126:15,17,21

126:22 127:10

127:11 129:1

130:7 131:17

132:2 133:14

133:16,19

134:1 135:10

142:18 143:13

143:17 144:7,8

144:15 147:5,6

147:9,12 148:1

148:14 149:4,9

149:11,14,20

150:1,3,9,15

150:21 151:3

151:19 152:15

152:21 153:5,9

153:22 154:18

154:20,21

155:5,11,16,16

156:11 157:16

166:19,22

167:3,9,12,21

171:18,21,22

172:2,4,10,18

173:4,9 174:4

176:7,20

177:17 190:4

191:20 196:20

197:7,15 198:7

198:20 199:2,6

199:8,17,20

200:8,10,14,14

203:2,5,6,22

204:1,4,8,10

204:21 205:2,6

205:10 206:11

206:16,19,21

207:16 208:2,3

209:2,13,19

211:2 215:10

215:13,18,20

215:22 216:4,5

216:13,16

217:5 218:5

220:12 221:1

221:10 222:12

223:17,19

224:10,12,15

224:21 225:2,6

225:12,16,21

226:2,7,9

227:2,19 229:5

229:15 230:12

230:13,16

231:2,3,10,13

231:14,18,22

232:5,9,18,20

233:5,10,11,17

233:18 234:6,7

234:9,14,16,18

234:22,22

235:8,13,18,21

236:18 237:6,8

237:11 238:10

238:12 239:12

239:22 240:2,3

240:8,19

242:16,21

243:9,19

244:11,13,18

246:12,14

247:3,6,18,22

250:5,7 252:1

255:18 256:2,5

256:8 257:1,11

258:12,18

259:16 261:12

264:4,9,18

265:10 267:16

267:20 269:5

269:22 270:4

270:11 274:3

275:7 276:4

277:6,7,15

281:1,4,10,11

282:6 284:13

285:5,14,15
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

47

287:5,18 289:6

290:5,7 291:1

291:3 293:22

295:1,20 296:2

296:6,12,19

297:4 298:16

298:16 299:3

299:13 302:14

304:15 305:1

306:3,10

310:11 311:18

312:2,4 313:20

317:10,11

330:1 332:2,4

332:7,21 333:1

333:3,17,18,19

333:22 334:2

334:12,22

335:19 336:1,8

337:2 338:17

339:20,22

340:5,11,17,17

340:18 341:2

341:12 342:2

343:6,7,15,18

344:5 345:9

348:15,17

349:9 352:21

353:7 356:21

357:6 358:21

362:4 363:7,11

363:20 364:3

364:20 365:3

365:19 366:18

368:13,20

369:2,7,12,15

369:21 370:7

370:18 371:2

375:2 376:1,3

376:11,12

383:19

privacypreser...

40:18 106:12

privacyrelated

224:13 225:18

private 3:17 5:2

5:7 6:7 14:17

27:17,19 42:17

42:19 43:5,16

43:22 44:6,8,8

44:16 45:7

63:1,13 102:11

102:14,15

103:22 104:4

104:18,21

124:20 125:12

125:13 127:16

127:17 133:19

133:21 141:7

151:16 152:20

186:9,18

216:10 229:7

231:16 233:12

234:12 250:3

264:7,8,19

267:15 284:19

285:4,20

287:20 288:14

290:14 291:14

293:3 294:14

296:10 297:6

297:14,17,19

297:21 298:2,7

298:10,14

299:10,16,21

302:13 303:5

314:5 317:15

317:21 327:21

328:7 329:11

331:4 336:5,8

341:20,22

342:5 343:12

345:2 352:17

354:9 356:13

357:4 367:17

379:18

privilege 287:15

325:4

proactive

218:22 267:22

proactively

205:9 322:2

probable 29:16

53:17 65:3,14

73:1 81:21

169:5 362:3

probably 27:14

58:15 101:5

120:16 137:6

191:17 276:12

306:13,18

307:10 317:16

318:17 337:1

342:22 343:2

344:13 360:10

probe 11:17

probing 41:3,5

problem 13:14

21:11 27:7

47:17,22 48:8

48:10 49:20

51:18 62:17

92:1,7 117:12

117:13 118:4

120:11 121:17

137:11 146:18

171:8 264:21

274:20 277:3

305:2 309:14

316:21 320:1

335:6 346:5

353:13,15

356:7 371:11

371:16

problematic

361:12

problems 20:16

20:20,21 21:2

46:12,13,15,16

46:17,20 47:1

47:2 48:7,8

50:5,5 51:17

51:17 72:17

76:15 103:3

104:12,15,18

104:19 105:4,5

105:6,7 143:7

189:15 193:3

203:19 256:19

311:9 344:7

376:16

procedural 28:1

procedures

114:5 177:13

300:5 314:22

376:2

proceed 4:20

134:19

proceedings 4:1

130:11 385:6,8

process 56:9

117:18 130:13

146:5 174:9

191:8 218:8

223:7 231:14

232:15,17,20

233:6 236:9,10

240:18 250:6

251:16 274:2

277:5 283:14

308:18 316:8

330:20 344:16

344:18,21

345:20 346:11

347:8 356:9

382:22

processes 29:21

57:21 96:2

117:21 126:9

127:3 132:18

173:22 215:4

224:6 252:13

330:22

processing 46:4

323:2 324:11

325:5

procurement

250:5,9,11,12

252:7

procurment

251:16

produce 164:4

246:11

produced 74:2

produces 16:5,6

product 26:17

35:8 126:14

150:22

products 111:19

113:2 125:10

125:18 131:20

150:18 154:20

professional

140:17 242:16

247:20

professionals

190:12

professor 18:1,3

33:11 45:10

54:8 89:19

110:12 142:8

169:17 186:22

194:7 286:21

287:2,2,6

296:14 305:22

326:9 328:2

335:22 341:17

374:4 375:17

381:9

professors 92:17

195:12

profile 75:14

148:9 268:16

profiles 159:10

162:12

profound

151:20

program 7:6

10:8 25:5

30:17 31:3
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

48

56:18 84:14

90:1,9 92:4

103:4 120:7

126:21 127:2,8

128:16,18

129:1 158:18

163:17 174:3

175:10 180:20

180:20 181:1

182:6 202:11

204:13 206:20

214:9 224:22

225:6 229:21

230:7 245:2,19

251:22 252:2

257:15 260:14

261:21 275:7

311:2,17 332:4

332:6,11

359:18 360:18

365:9,22

366:14 372:1

programs 30:15

32:2,7 46:3

56:16 58:14

64:6 81:6,18

128:7 129:3,7

130:5 168:14

180:14 199:18

200:21 201:2

204:15,18

205:4,7,12

213:21 214:5

215:18 216:2

227:3,19 241:9

241:9 242:14

245:16 247:14

262:13 296:4

310:2 311:7

331:21 333:9

375:19

progress 153:13

154:7,14

234:21 284:8

prohibited

122:20

prohibitions

128:19

project 311:12

prominent

122:4

promise 99:4

122:10

promised

156:16

promote 154:21

234:9

proof 225:1

proper 23:14

48:18 74:15

210:3 315:3

proportion

317:13

proportionality

317:6 345:4

362:14 366:3

proposal 212:9

proposals

358:17

proposed

354:11

proposes 358:8

proposition

96:15 233:2

proscribed

334:20 365:11

proscriptions

324:8

prosecute 16:1

prosecutor

70:15

prospect 95:12

prospective

267:19

protect 9:1 20:2

20:3,6 24:3

28:20 29:4,20

29:22 31:11

101:17 111:2

113:21 116:11

118:16 121:14

124:6,8 133:6

167:14 199:5

204:11 208:3

228:9 230:16

233:9 237:7

258:12 264:10

264:12 289:5

330:1,22 333:2

333:22 334:3

369:1 376:11

protected 31:10

42:11 114:1

126:4 133:17

150:8 167:17

191:14 211:4

222:11 264:5

protecting 28:16

52:7 61:11

129:1 133:19

142:18 234:5

242:21 284:13

287:19 289:5

306:3 341:12

341:14 367:7,9

protection 42:4

46:2 113:14

114:5 154:3

168:20,21

169:4 194:20

194:22 200:9

200:11 206:5

216:14 229:20

258:16 290:7

290:11,12,17

296:2,7,20

297:4 299:4

306:10 332:2

349:14 362:10

376:12

protections

24:14 43:4

52:17 85:10

86:13 111:3

132:8 163:5

166:1 204:19

208:2 221:1

225:17,21

229:15 232:2

233:7,14

234:17 280:22

281:1 282:8

296:16 333:1

341:9

protective

299:12 332:7

protects 12:5

20:7 125:18

167:15 207:16

209:2 217:5

304:15 348:17

protocols 51:8

proud 28:12

prove 113:9

proved 134:5

proven 235:2

366:18

provenance

326:4,5 365:17

provide 24:12

84:16 105:13

112:17 130:16

135:5,20

168:20 169:2,3

210:9 211:11

211:22 213:14

214:1,15 218:1

239:15 276:2

294:19 295:5

301:9 302:3

305:18 370:8

provided 43:4

175:13 219:5

provider 182:16

188:4,20 189:5

providers

129:22 181:2

202:1

provides 100:9

113:13 216:2

221:11 225:14

226:18

providing 24:2

83:8 85:10

104:8 111:3

115:10 135:8

207:12 209:6

210:12 211:8

227:15 249:12

266:7 272:5

280:4

provisions

122:14 365:6

proxy 274:10

pseudoprivate

107:15

psychologists

122:22

public 1:12 4:4

5:4,7 7:10 14:1

14:4 15:2

33:12 48:4

66:2,2,5 67:16

74:2 75:2,4

84:8 96:14

115:5,11 120:1

129:2 130:22

148:14 163:14

180:7,8 197:17

207:8 211:10

213:3,6 255:5

278:21 284:8

287:20 288:15

291:14 292:18

298:15 317:20

328:5 329:12

330:5 336:5,18

337:2 339:21

340:1 381:7

385:4,17
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

49

publically 219:4

publicity 5:8

publicly 14:19

15:3 75:11

92:3 208:12

339:1,13

publish 129:17

published 82:5

publishing

271:13

pull 203:17

316:7 323:21

345:21

pulled 60:16

pulling 58:16

purchased 37:6

purchases 35:8

purporting

271:9

purpose 69:9

94:9 97:18

118:9 256:12

260:17,18,19

261:1 265:1,1

272:11 275:2

288:21 289:2

296:16 300:16

300:18,21,22

329:6,7 359:19

359:20 360:1

362:16 366:19

purposeful

178:11

purposes 79:20

93:13 112:2

186:19 273:5

281:20 299:9

318:4 352:22

365:15

pursuade 243:6

pursuant

204:18 278:20

pursue 57:14

221:9

pursued 260:20

260:21 280:3

pursuing 213:18

push 96:5

201:22 235:20

269:3,13

put 7:7 17:4

27:6 39:8

52:20 58:22

83:2 93:22

144:12 150:3

150:12 152:11

160:15 163:6

167:1 169:9

172:22 201:15

229:11 242:3

269:14,18

280:5,8 370:21

377:15

puts 25:18 38:20

245:18

putting 60:6

151:8 268:5

Q

quantify 164:8

196:21

quasifederal

106:19

queries 164:10

querying 60:13

question 9:15

10:1 26:9

31:16 43:9

55:7,8,11 61:8

63:19 66:19

74:15 75:3

76:9 82:20

90:16 96:2

98:11 102:18

105:19 107:21

110:8 142:1

154:16 155:14

159:22 165:16

170:1 171:12

174:1 175:9,19

179:5,13

181:12,20

182:13 183:8

185:4,5 186:14

192:2,5 195:1

196:18 197:9

197:10 199:9

199:15 200:6

212:16 238:9

238:16 239:11

239:18 241:5

241:12 252:15

255:13,22

257:2 258:11

262:9 267:14

274:12 276:13

276:15 279:5

281:3 282:17

283:4 286:18

289:4 293:5

313:16 318:10

318:12,15,20

321:20 326:10

334:9 335:18

344:11 348:7

357:22 358:10

358:16 360:14

361:11 363:5

363:22 367:14

368:16 370:11

372:19 373:5

374:3 379:4

questionable

112:22

questioning

74:11 110:3

141:17

questions 6:11

6:12,14,16

9:17,20 10:2

41:1,3,6,14,17

81:16 88:8,10

91:16 100:9,20

100:22 101:1

102:3 109:1

110:5 133:22

134:13 141:1

141:15,21

150:7 159:16

165:5 175:21

181:9,14 185:8

194:6 206:1,3

215:6 230:6

232:5,7,10,15

237:19 238:11

238:20 239:3,9

248:5 255:2,4

260:15 262:12

262:14 263:2

274:5,15,19

275:10 277:9

278:3,6,8,12

278:15 280:3

282:20 284:16

286:9,11,12,14

312:11 318:6

322:5 343:16

357:7 368:9

381:7 383:6

quick 66:12

71:3 76:17

78:3 175:9

179:12 183:19

198:18 203:12

313:15

quickly 51:4

53:3 146:6

175:22 198:11

200:12 267:14

277:14 354:7

quite 12:19 17:7

70:11 74:4

87:12 151:20

224:3 240:5

246:10 249:16

252:8 318:3

337:8

quorum 4:13

quote 8:7

136:20

R

race 68:18

rachel 2:4 4:13

80:14 159:18

271:4 285:7

racial 122:1

radically 148:9

raise 110:9

276:4 305:4

357:7

raised 74:15

326:9

raises 357:1

ramification

17:5 140:12

ramifications

13:2 137:12

139:14 202:4

range 11:10

101:20 152:7

172:13 253:8

319:1 383:11

ranging 237:10

rank 319:19

rap 14:13,15,17

rapid 226:15

rapidly 190:6

rare 12:5 295:3

rarely 133:12

rat 13:13

rate 57:1 58:16

315:21

rates 315:22

ratings 184:17

ravaged 137:19

reach 91:11

249:17

reaching 33:6

reaction 353:2
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

50

reactions 360:11

reactive 267:19

read 31:4 45:19

58:7 83:6

181:13 339:10

351:17

readily 75:5

160:19 273:22

reading 103:4

162:22 220:9

241:9 301:19

339:15,16

reads 324:15

real 77:10 97:6

97:6,7 113:13

132:3 184:4

252:21 257:1

266:21 292:3

328:21 331:2

379:12,22

realistic 221:7

realities 99:15

reality 77:10

84:15 97:21

realize 33:1

121:21 148:12

156:22 167:9

357:1

realized 12:16

210:16 347:12

really 13:20

25:13 27:9

32:6 47:6 48:7

53:9,10 55:4

58:10 67:9

68:18 71:3

72:20 76:10

84:3 88:21

89:20 93:6

107:21 116:3,6

120:16 143:3,5

143:6 145:7,20

148:22 150:4

155:22 157:1

157:20 158:13

158:13 159:21

161:13,17

163:10 170:3

177:3,3,6,15

177:16 178:5

187:3,4,5,6

190:1 195:21

196:4,4 197:4

197:5,10,10

200:13 220:5

225:4 239:4

248:11 250:7

251:16 252:7,8

256:21 259:1

259:10,17

263:3,22 270:3

271:10,18

272:4,8 275:9

277:12 282:1,2

282:21 287:16

287:17,21

288:15 289:3,6

291:4 292:15

294:18 307:6

308:9 312:12

313:14 316:2

323:14 326:14

328:7,8 329:13

330:19 332:21

336:6 346:13

357:16 360:14

373:6 374:16

378:20 381:5

reason 15:17

25:17 53:9,11

69:14 82:4

121:8 155:9

175:6 247:8

291:5,12 292:1

292:15,22

293:1,2,2,3,4

339:7 349:3

362:5,17 372:3

reasonable 23:1

60:12 65:10

70:4 111:11

147:5 148:13

157:20 162:7

169:4 198:7

292:17 334:11

362:2 363:7,10

363:19 364:2

366:15,17

368:12,13,20

369:2,6,11,15

369:20 370:6

reasonableness

65:16 70:17

377:8

reasonably 59:2

69:20 106:4

155:2 177:18

182:11

reasoned 327:9

reasons 79:1

264:2 268:4

292:20 299:15

319:6

reassert 68:2

reassociate

196:14

reassurance

211:11

reassure 120:17

267:2

reassures 56:9

rebecca 3:14

recall 70:21

321:5

receive 5:18

88:6 136:7

332:19

received 128:9

129:13,19,22

139:10

receptacle 8:14

recipient 139:22

recognition 42:7

55:2 148:3

362:7

recognizable

54:10

recognize 8:22

54:11 64:11

68:1 75:18

123:6,18 125:7

131:18 153:9

155:3 231:5

270:16 284:5

327:18

recognized

42:11 65:5

133:13 151:13

217:12

recommend

61:11 93:13

303:1

recommendat...

92:15 213:19

240:12 373:9

recommendat...

92:11 128:15

171:16 239:15

279:19 288:1

300:1

recommended

163:22

recommends

93:16

recommit 303:2

reconcile 55:1

157:13

reconciled

315:13 316:1

reconstruct

39:18 184:10

reconstructing

179:5

reconvene 203:1

record 59:20,21

97:9 109:4

175:12 203:3

284:21 306:21

385:8

recorded 7:6

34:9 163:20

330:10 385:7

recording

329:15

recordings

34:11

records 14:11

14:16 15:16,16

98:21 99:5

120:7 163:3,17

168:1 221:12

221:14 222:6

278:19 301:14

301:17

red 2:14 17:21

22:14 25:5

110:2 207:6

286:4,6 354:4

redacting

307:12

redactions

220:11

redefine 118:12

redress 83:15

282:9 294:11

294:15 295:4,9

reduce 40:11

174:15

reduced 174:22

reducing 211:7

redweld 306:14

307:1

reevaluate

134:3

refer 355:5

reference 75:6

referenced

74:16

referendum

361:1
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

51

referred 126:17

186:4 192:3

referring 68:4

185:21 326:17

refers 97:12

refine 232:15

reflect 97:9

170:21 355:15

reflected 174:10

229:17

reflects 29:13

382:5

reform 127:22

130:15 168:5

179:21 212:3

380:16

reforms 130:3

refrained 16:14

regard 33:3

126:1 128:1

132:6 219:8

220:8 221:19

222:9 246:4,13

281:22 285:6

294:15 317:17

regarding 5:2

6:4 87:4

204:12 213:20

272:6 278:21

301:10

regardless

118:22 168:6

231:8 303:5

365:1

regards 220:14

regime 230:11

register 4:10

regnant 86:8

regular 224:11

240:17 373:19

regularly

133:22

regulate 46:19

78:22 211:16

regulates 103:1

regulating 24:11

38:14

regulation 42:19

43:7 102:13,19

103:11,12,14

103:19 108:6,7

108:12 290:17

regulations 7:11

384:5

regulatory

372:18

rehash 354:3

360:15

reidentified

113:11 114:2

reidentify

160:18

reigning 44:15

reinforcing

184:3

reiterate 341:9

relate 47:2

183:4 285:5

related 49:9

124:20 128:13

232:7 241:4

295:17 344:7,8

relation 21:21

relations 115:11

relationship

63:17 167:16

239:14 302:20

337:22,22

338:1

relationships

54:20 153:3

242:10 263:18

265:13

relatively 76:2

225:13 298:3

release 155:20

released 157:8

222:15

releasing 14:9

164:9 201:20

relevant 28:3

100:12 125:22

134:7 203:14

206:15 289:1

301:4 307:4

352:7,8 370:16

relied 375:6

relies 121:9

religion 11:3,16

11:17,19,21

12:2,3 26:16

44:10

relinquish 13:22

relocate 122:16

rely 74:18 124:3

190:9 194:1

246:10

remain 45:8

119:7 122:12

125:12,21

228:6

remaining 86:8

101:12 154:15

223:22 274:18

remains 44:5

remark 278:17

340:10

remarkable

375:9

remarkably

383:7

remarked 8:4

373:15

remarks 7:2

45:14 109:20

140:22 141:14

157:22 172:6

217:1 237:21

281:12,14

284:15 286:2

remedial 281:5

281:8

remedies 281:10

remedy 282:6

remember

106:21 119:18

135:21 136:3

remind 88:9

109:19

reminded

203:20 214:21

reminder 255:3

318:7

reminders

214:16

reminds 159:2

remotely 197:14

render 214:7

renee 7:15

109:22

renting 321:7

repealed 123:12

repeat 198:8

repeatable

232:16

repeatedly

298:15

replace 101:9

350:5

report 50:14

74:3 82:5

84:11 119:12

162:22 179:20

184:17 213:20

223:14,15

256:9 279:19

351:17,19

358:1 362:19

reported 1:22

130:20

reporting 130:2

220:16 240:10

302:6 336:12

346:21

reports 31:5

57:16 163:19

177:4 206:11

253:20 302:13

repository 21:14

49:16

represent 45:15

197:14

representation

364:16 380:7

representative

32:21 253:12

represents

155:7

republican

69:22

repurpose

382:15

repurposing

322:21

requested

365:15

requests 167:21

168:14 302:5,8

320:22

require 30:22

59:4 168:5

218:22 219:1

219:14 228:8

370:21 371:4

required 102:13

168:10 172:12

208:3 272:16

298:22 346:21

requirement

29:17 71:21

202:5 311:16

314:10

requirements

174:4,5 227:8

239:16 272:19

327:6 343:7

requires 31:18

52:11 91:4

130:12 131:4

188:19 219:9
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

52

requiring

111:18 112:17

370:14 371:12

research 18:3

39:15 92:2

144:4 234:5

250:15 251:10

251:14 252:10

349:19

researchers

144:3 160:16

reservation

321:8,9

reshapes 226:11

reside 231:9

resides 156:2

resilient 111:16

resist 118:18

121:9

resisting 377:4

resolution

170:15,17

resources 68:17

150:2 160:19

194:4 245:13

248:7 294:22

310:3,8 319:8

343:2

respect 45:3

90:13 97:17

115:11 179:6

193:2 198:2

231:8 334:10

361:11 365:21

380:3,5

respectful

189:15

respond 158:22

348:19 351:12

responded

298:15

respondents

16:10

response 57:9

70:8 127:3

227:7 253:22

262:9 305:2

355:4 357:21

responses

165:17

responsibilities

245:10

responsibility

240:13 244:1

responsible

18:20 19:1

209:15 236:22

responsive

298:19

rest 88:16 190:6

293:16 327:12

327:16

restore 128:5

restrain 29:22

restraining

27:20

restraints 300:9

restricions

245:20

restricted

300:13

restriction

42:16,17

121:13 122:12

245:19 301:2

376:20

restrictions

81:10 118:17

121:10 124:3

245:18 341:18

result 176:9

187:19 228:21

266:18 315:20

379:3

results 177:4

290:2 330:8

resume 109:3,6

284:18

retailer 35:7

retain 62:1

118:2

retained 126:2

221:4 222:4

300:17

retaining 134:5

263:20

retention 40:20

90:4 93:2

256:10 257:22

258:4 259:3

322:6,11 351:9

351:10 371:8

retrospective

347:1

return 195:6

returned 222:1

returns 192:13

319:12

reveal 76:14

151:15 153:2

revelation 47:13

revelations

128:6 131:15

reverse 159:17

194:15

review 30:9 31:1

57:20 59:9

60:3 65:19

71:1 74:1

116:1 130:12

168:13 221:11

221:13 223:6,8

223:17 251:2

253:18 281:20

371:1

reviewed 358:3

reviewing 30:5

reviews 30:3

173:4 216:1

223:5 281:16

revocation

201:18 202:3

202:14

revoke 201:22

202:16,18

rewriting

366:22

rhetoric 294:2

richards 3:14

225:11 226:4

238:9 250:10

262:10 268:22

271:11 272:9

273:10 277:1

283:6 354:20

rid 52:8

ridiculous

197:19

rifle 307:2

rigged 200:15

right 4:21 5:1

17:22 19:16,17

26:17 48:14,17

51:5,5,14,16

51:20 53:2

54:4,10,15,15

57:11 60:5

65:2 66:22

72:10,12,18

76:6 77:9 82:8

82:12 83:14

84:5 85:16

87:1 88:20

90:15 96:21

97:1,17,18

99:20 107:18

117:12 118:10

118:22 124:9

129:12 132:3

134:1 136:4

138:10 139:7

140:16 146:9

146:10,11,20

147:14 153:1,1

153:10 157:4

158:17 160:13

161:7 173:9

175:3 176:17

178:7 181:7

186:14,21

195:21 196:2

197:9 200:18

201:19 202:15

203:21 220:7

241:19 242:22

243:1 247:15

248:5 251:2,3

251:13 252:8

253:5 258:8

259:12 261:18

262:7 263:12

263:17 268:6

275:10 285:9

293:16 310:19

314:2 315:3,6

326:3 333:19

335:15 338:15

344:16 345:12

347:6 350:19

350:20 361:19

362:1 364:16

364:21 365:10

365:20 367:14

378:9 379:13

380:5

rights 8:12 11:2

19:2 51:7

83:15 84:20

99:2 130:8

131:17,22

150:15,21

157:6 191:22

202:1 226:21

234:11 295:9

329:19 378:10

riley 133:14

151:13

ripe 59:21

rise 114:22

risk 13:17 40:11
Meeting November 12, 2014

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Henderson Legal Services, Inc.

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49:15 60:18

62:5 92:19

93:1 106:6

111:12 114:14

115:12 119:16

159:6,10

162:12 163:4

183:20 235:8

235:17 236:9

236:10 258:18

259:16 262:3

263:2 275:6

280:5 290:18

290:18,18,22

291:3,12,21,22

330:21,22

331:16 334:6

337:11 350:10

381:15 382:1

risks 43:19

45:16 49:11

58:2 61:5 93:4

104:2,9 162:13

225:2 231:22

232:18,22

233:5 236:1

237:7 256:8

258:10 261:5,7

261:8 262:1,2

266:21 275:4

291:6 343:12

risky 235:21

270:4

road 259:11

roberts 133:13

robust 84:20

125:15 210:5

211:2 252:20

253:14 297:4

303:2

robustly 299:16

rogue 346:19

365:18

role 32:14 65:20

73:21,21 74:6

74:7,9 83:13

116:20 207:12

225:13 239:2,5

239:18 247:3

305:10 317:1,2

372:13 373:7

374:6,8,9

375:18 376:22

377:18 378:14

379:5 380:3,10

rolebased 127:5

172:14

roles 12:3 127:1

127:7 172:17

246:2 355:12

roll 288:11

room 22:11 60:9

78:22 115:22

116:4 137:7

144:22 165:12

193:14 201:14

304:21 329:14

rosenzweig 2:14

25:4,9 33:9

50:8,11 53:20

55:6 58:5 69:5

76:17 84:10

94:14 98:5

99:7,18 100:21

101:11 106:17

107:7

rough 163:15

round 110:3

141:17 146:12

route 245:5

routine 222:15

222:17

routinely 123:1

229:10

row 9:11 109:17

109:22 116:16

185:5,6 186:21

207:6 286:3,20

rubber 259:11

rubric 9:5

rule 38:19 99:10

126:7 131:6

135:2 159:16

315:5 371:22

375:12

ruleladen

325:14

rules 28:6 38:11

95:1,8 113:7

132:19 135:4,9

135:12 140:15

157:21 158:2,3

158:5,9,16

159:11,13

168:12 169:11

169:15,21,21

171:6,21

172:20 174:10

190:8 192:16

210:22 211:17

211:20,22

212:1,4,13,18

213:5,8,9,12

213:13,13

238:18 335:13

343:18 344:9

348:6,12 356:6

rulings 130:12

run 37:22 55:22

126:19 188:21

336:2 360:10

running 130:21

182:7 200:8

343:10

russians 189:13

S

s 3:13 36:4

127:19,20

128:4 129:10

129:14 131:15

131:17 132:1,9

132:12 133:2

164:5 230:2

278:22 279:2,3

279:21 282:9

284:6 289:9

292:4 301:8,8

302:10,11

343:17 369:7

sadly 57:4

safe 133:1

safeguards 87:4

safer 40:10

safety 40:6

133:7,8

sake 20:7,8 27:3

192:14

sales 127:6

172:16

salient 27:15

salt 27:6

sam 286:2

sample 184:2

samples 135:20

sampling 164:4

sanchez 66:16

sanctions 95:7

sand 300:10

sat 136:20

satified 210:2

satisfied 43:3

177:19 187:15

satisfy 157:21

316:5

saw 164:1 278:7

300:11 375:1

saying 47:6

150:20 160:2

163:19 167:1,5

167:8 178:20

178:22 187:8

260:8 284:2

298:10 310:20

310:22 348:2

350:10 351:15

353:14,17

354:14 381:13

says 72:9,12

96:4 163:1

314:16 332:21

342:3 347:1

349:9 352:21

353:8 357:12

357:14 362:8

381:10

scale 50:17

52:20 73:15

152:18 169:2

248:2 260:5

261:6 302:18

318:3,5,5

334:8 344:7

349:17

scared 373:5

scenarios 86:11

167:10

schedule 222:5

schemes 281:8

scholar 115:15

scholars 113:4,8

113:12

scholarship

36:17

school 3:18 18:4

110:13 117:6

161:12 196:1

287:3 292:5

science 33:12

77:11 110:15

233:17 234:7

234:17 261:15

313:11 316:8

335:15,16

sciences 181:16

scientific 235:1

236:9,15 237:5

scientist 33:19

134:18

scientists 237:11
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

54

237:13

scifs 253:18

scool 2:15

scope 164:1,8

301:3 302:4

334:8

scratch 53:19

65:8

screen 174:9

screening 59:16

84:19

scrutiny 29:12

60:16

seal 385:12

search 42:3 52:9

65:10 198:13

198:14 300:12

searched 91:20

searches 52:10

53:8,18 70:10

72:10,13,15

198:14 290:5

314:20 369:1,4

searching

192:22

second 5:22

13:22 19:12

34:2,13 37:14

70:1 109:10

111:10 115:2

116:16 117:2

119:2 128:21

199:10 202:22

238:1 269:11

285:16 290:13

297:18 301:9

324:14 332:12

334:21 346:11

364:18 381:15

secondary 46:4

58:17 84:18

secondly 186:15

319:10 373:4

seconds 32:13

76:11 105:21

secrecy 32:1

192:8 208:10

208:21 210:21

373:8

secret 66:4

98:18 214:18

302:2 355:17

355:18

secretary 25:7

secretly 301:15

secrets 18:8

19:7 32:10

115:3 208:11

214:11,17

267:9

section 90:1,9

90:14 92:3

103:5 119:13

128:16 175:10

278:20 301:4

301:11,15

330:11 351:17

369:5

sector 3:17 6:7

43:5 104:18,21

124:20 127:16

127:17 133:20

133:21 174:4

186:9,19

200:16 216:10

230:18 231:16

233:12 234:12

250:4 264:7,8

264:19 267:15

285:4,20

288:15 290:15

291:14 293:3

294:14 296:11

297:6,15,17,19

297:21 298:2,7

298:10,14

299:10,16,21

302:13 303:5

304:9 310:20

314:5 317:16

317:21 327:21

328:8 329:12

329:12 330:6

331:4 336:5,6

336:8,18 337:2

341:20 343:12

345:2 352:17

356:13 357:4

367:17,17

379:18

sectors 284:19

287:20 342:6

secure 12:21,22

18:20 49:10,11

72:10,12 94:7

113:2 138:4

143:21 155:16

209:21 253:17

secured 256:17

security 3:7,14

8:18 10:8

14:11 19:15,17

19:21 23:1

24:15 25:3,8

29:14 42:2

46:3 49:8 54:1

61:18 64:6

66:8,13,18,20

67:4,5,6,9

69:11 73:6,12

73:13,14,17,18

81:1,12 83:18

84:13 85:10

89:9,13 100:5

104:6,10

111:20,22

112:12,18,20

112:21 114:20

115:20 116:13

123:22 126:4

129:14,18

132:22 133:7

133:20 134:15

135:7,9,13,14

140:9,16,18

143:13 155:19

156:21 162:17

166:13 176:13

176:19 188:8,9

188:11,15

190:3,12

200:11 204:5

218:16 219:19

223:2,4,5

225:3,12,22

226:3,15

229:10 246:19

256:21 257:1

257:10 272:12

280:1 285:7

290:4 291:2

293:2 294:10

294:19 296:3

297:20,22

298:8,18 299:8

302:5,7 303:12

303:14 310:20

313:20 321:15

327:11 330:20

331:3,12 333:9

335:12,19

338:21 339:7

343:11 345:8

367:1,7,9

375:19 381:21

382:13 383:19

see 6:6 19:9 24:1

24:2 27:12,21

49:8 50:1,21

55:7,8 56:6

57:4 59:14

69:14 76:7

86:8 95:16

104:20 116:6

135:13 142:14

144:10,13,22

145:5 148:20

148:21,22

150:2 155:14

160:17 177:2,9

183:22 190:20

198:1,9 199:4

223:10 236:22

245:5 249:10

256:12 266:11

280:16 290:15

294:13 307:1,3

307:7 308:6,12

320:14,16

341:1,3 342:10

372:11 376:20

seeing 57:8

158:18,18,19

188:2 290:14

297:8 338:16

370:12 376:16

seek 125:14

131:2,5,20

222:8 365:5,6

seeking 132:12

241:14

seemingly 35:12

36:14 89:5

seen 19:16,17

44:12,13 95:18

109:22 129:4

337:4 374:21

sees 18:15

segment 198:3,4

segments 198:2

segue 159:21

seinfeld 321:7

seized 290:9

292:8,10

seizure 42:3

seizures 72:11

72:13 369:2,4

select 174:7

selected 226:6

selfcorrecting
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

55

158:20

selfevident

335:3

selfhelp 23:22

68:5,19

sell 131:20

semiclear

158:11

senate 117:7

164:1 179:16

303:11

senators 193:8

193:12

send 9:21 136:7

193:14,17

sends 343:17

senior 25:6

224:10 226:1

238:13 239:22

295:15

sense 28:5 31:10

83:18,19

105:19 151:6,7

161:18 163:15

166:21 221:11

244:1 246:6

248:11 251:4

271:22 273:8

274:8 317:18

320:11 322:6

338:2 344:13

365:14

sensitive 35:11

35:12 115:6

120:20 168:8

268:11,16,19

sensitivity 152:2

172:10

sensor 17:3

sent 137:2

139:10 326:13

sentence 105:17

separate 8:1

167:6

separately 62:13

sequential

235:10

serendipitous

50:18

series 8:20

29:20 198:1

206:1

serious 193:3

194:5

seriously 171:21

177:20

serve 361:10

served 204:15

206:16 288:21

303:11

server 156:2

serves 12:13

67:7

service 26:21

106:20 107:13

112:15 122:19

123:4,13

126:14 127:7

129:22 139:22

150:22 152:9

181:2 182:15

188:3,19 189:4

202:1 208:13

208:18 299:6

328:11

services 34:10

125:8,11,18

126:19 127:17

131:10,21

140:1 150:18

151:11,17

155:12 158:4

188:19 199:3

228:5 299:2

serving 122:20

123:12

session 2:9 3:1,9

3:16 202:22

203:7

set 108:10

113:10 125:15

163:2 222:5

231:12 232:10

264:21 271:22

299:22 316:21

322:9,10

374:10

sets 117:16,19

167:15 323:16

325:17 335:1,4

359:21

setting 69:18

86:19 294:14

settings 114:3

197:17

settle 379:14

seven 9:10

109:20 137:14

137:16 286:1

severely 377:2

shame 28:11

shannon 7:14

shape 20:4

226:20

share 13:7,9

17:10,12,13,14

105:2 198:3

220:17 264:20

375:14

shared 221:2

222:20 283:3

shares 13:5

sharing 17:16

119:20 123:11

142:5 149:17

241:12 325:19

sharon 7:14

sharply 112:8

sheer 37:19

228:19

sheet 14:13,15

14:17

shes 70:8

shift 371:2

shifting 213:4

213:11 251:21

259:3 330:1

shipping 126:14

shocking 163:13

shoes 361:9

shop 238:8

239:13

short 86:1

117:21 137:18

344:15 361:1

shortcuts 350:1

shot 67:18 99:13

shouldnt 18:10

55:4 67:9

95:18 171:8

180:2 311:22

361:16 375:7

show 65:2 267:6

267:6 283:15

283:16

showing 16:9,13

shown 37:12

177:5

shows 11:10

39:15 67:13

shredder 307:10

shut 283:2

side 19:20,21

58:14 66:11

68:20 73:13

130:13 142:12

142:13,13

155:6 170:2

216:16 219:17

219:18 221:16

222:10 261:6

261:15 264:17

267:5 277:22

371:17

sides 213:15

256:21 382:2

sign 126:15

167:1

signal 355:21

signals 231:6

320:2

signant 227:22

signed 51:9,14

354:15,16

significance

54:12 98:13,14

185:13

significant

20:15 43:19

58:15 59:11

216:19 218:1

223:6 228:21

234:21 240:1

253:1,11 309:5

314:16 338:4

361:3 367:13

significantly

240:5 248:18

248:19 306:9

silicon 135:1

311:12

silly 290:2

silver 172:7

332:9 370:7

simialar 268:7

similar 29:14

122:18 212:7

239:10 264:20

266:3 267:17

278:21 302:13

320:5 344:6

355:12

similarities

264:12,15

similarity

264:17

similarly 37:10

simone 7:15

simple 136:17

137:9 329:1
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

56

135:20 141:4

155:13

simplest 124:5

simply 28:10

62:18 84:12

106:1 113:1

115:8 143:15

156:20 175:14

314:7 322:21

323:12 345:8

364:10 370:17

simultaneously

187:22

single 40:2 62:1

98:9 121:8

140:3,3 168:11

168:11 270:17

sit 126:22

272:18 316:16

sites 39:12

sits 108:7

sitting 6:22

22:11 69:21

174:7 286:3

306:14

situated 205:2

situation 99:12

situations 8:20

259:19 348:20

six 16:13 45:16

177:2 273:16

274:18

sixteen 45:15

48:20 124:12

149:6

sixteenplus

125:1

size 58:9 101:19

152:14

skeptical 100:18

skew 73:12

skews 19:20

skill 305:10

skin 35:8 37:6

skipping 309:3

sky 136:22

skype 137:3

sliding 169:2

slight 199:13

slightly 21:7

269:16 316:20

sloppy 362:7

slow 121:21

123:18 379:20

small 51:21 59:7

66:4 242:9

244:21 245:9

246:5 299:22

310:6,6 371:7

371:10 374:11

smaller 44:4

138:12,12

228:8

smarter 110:22

187:11,11

smartphones

139:6

smile 8:6

smorgasbord

297:2 326:19

smudges 171:1

snapchat 156:15

157:11

snooping 24:4

snowden 16:9

16:14 57:9

120:6 163:14

198:12

social 14:11

17:3 19:17

27:2,8 37:12

197:16,22

199:7 229:10

237:11 338:14

338:22 340:3

sociatial 227:21

societal 8:1 9:4

19:13 20:2

26:21 33:6

155:10 289:18

296:14

society 8:15

11:4 17:2

19:18,20 20:3

20:4,9 22:6

27:4 47:20

50:19 54:13,16

54:22 55:3

56:10 57:7

67:7 70:6

121:21 123:17

137:21 147:17

167:15 191:8

229:4 291:19

302:22 312:5

societys 20:8

societywide 17:1

software 111:20

126:12 156:5

165:8 172:15

173:2 176:21

177:12

sold 24:6

solely 124:2

solid 380:7

solitude 5:6

solove 2:15 18:2

18:5 45:11

46:7 51:21

52:1 71:3 76:5

83:4 93:5

104:11

soloves 54:8

solution 315:14

316:10 358:21

solutions 115:18

285:19,20

311:8,9 316:7

341:21

solve 143:6

146:18

somebody 47:7

47:8,12 54:4

55:5 60:3

61:16 175:4

186:4 201:10

245:4 249:17

353:17

someones 156:2

268:16

someplace

275:16 292:11

somewhat 25:21

33:15 100:18

158:20 173:18

183:12 230:13

240:10 255:12

256:3

sophicated

280:7

sophisticated

41:9 111:1

112:22 142:4

199:4

sorry 51:1

193:16 194:17

201:11 207:3

255:8 259:5

262:6 263:10

287:13 318:16

sort 26:11 30:21

55:13 57:8

60:3,11,19

76:19 81:15

87:12,13 93:6

104:13 105:20

106:2 150:12

152:19 159:17

172:19 175:20

180:10 192:14

210:14 218:5

225:1 243:10

246:2,17

250:20 251:21

252:3 255:13

259:20 260:17

261:6 262:19

263:1,3,6,14

268:3 269:5,10

269:12 270:14

271:2 273:11

274:2 275:1,17

277:9 283:12

284:1 289:15

290:2,21

294:16 306:1

307:22 329:4

330:20 333:18

335:14 338:12

338:20 340:3

340:22 352:2

366:13 368:12

369:10 378:3

379:3,20

sorts 91:21

soul 192:22

sound 136:22

177:18 328:22

329:1

sounds 88:2

197:20

source 323:13

sources 89:7

214:18 323:13

soybeans 193:15

space 5:2 138:3

304:7 338:15

349:22

spam 188:22

spare 304:2

speak 10:5

20:10 25:11

117:10 124:16

151:22 249:5,7

295:22 303:16

speaker 110:11

speakers 383:4

speaking 14:4

24:5 238:15

265:15 284:7
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

57

360:2

special 358:18

359:1 376:7,13

376:18

specialist 248:3

specialized

233:9

specific 36:15

45:21 62:8

90:11 92:7,14

128:11 168:14

172:15,16,17

173:18 182:8

214:4 220:15

234:15 244:15

262:8 280:12

287:22 300:16

300:21 312:13

336:19 352:7,9

352:10 359:18

361:11

specifically

42:11 68:4

98:22 153:19

180:13 182:3

208:1 240:6,7

247:3 280:11

281:19 311:6

314:21 315:1

specification

272:11 329:6

specificity 382:3

specifics 279:9

specified 300:22

329:8

spectrum 205:6

speech 11:3

355:7

speeches 9:15

spend 30:4

233:16 239:7

275:5 313:9

spent 68:17

333:21

sphere 56:7 85:5

208:9,21

210:21 211:1,7

spillway 351:2

spoke 164:20,22

spoken 120:14

sponsors 311:11

spread 227:12

springing

333:14

square 142:19

squared 32:1

staff 7:14 110:6

116:21 193:20

242:9 248:7

249:19 250:19

253:3,13,16

254:2,5,8,13

254:17,21

286:14,18

303:11 310:6,6

staffer 193:8,13

193:16,17,19

303:18

staffers 193:14

254:11 318:8

staffs 245:9

stage 34:1,5,13

35:4 92:21

174:9 372:20

stages 57:18

218:6

stake 59:10,12

101:14

stakeholders

308:21 361:9

stand 153:11

194:13 233:19

286:15,16

361:9 378:22

standard 55:21

70:17,18 74:16

75:16 81:5

82:17 167:4

198:9 222:18

233:7 354:8

358:19 359:22

367:10

standards

111:21 125:16

125:16,20

126:7 153:14

163:2 196:19

200:14,15

314:11

standarized

232:14

standing 327:6

start 10:6 23:22

27:11 41:22

42:13 49:22

55:4 67:19

74:12 89:19

93:4 96:20,22

148:12 166:8

174:2 191:1

192:11 207:5

250:10 255:7

257:18 269:10

269:13,17

273:13 274:17

275:16,17,18

286:1,14,21

287:6 306:5

312:2,20

318:18 324:20

338:13 343:21

347:17 364:1

started 139:7

198:14 210:19

242:7 250:11

304:6

starting 53:18

54:16 65:7

76:8 96:19

100:22 159:18

171:17 219:4

274:15 283:8

374:3

starts 47:11

283:7 379:12

stat 157:8

state 26:4,5

115:3 138:2,4

157:20 187:16

189:17 190:1

220:19 302:20

348:11 385:5

stated 262:21

273:12

statement

109:14 138:2

276:10 277:6,7

318:21,22

319:3 326:13

326:18 329:17

statements 99:9

109:15 136:3

224:4

states 51:8

127:22 193:8

228:4,6 343:22

statistical 164:4

status 35:18

37:12 229:22

statutary 265:1

statute 301:19

statutorily

281:7 282:11

statutory 5:19

122:10 204:17

207:19 240:13

375:21

stenographica...

385:7

step 46:18 76:16

129:20 156:1

163:12 177:16

323:4 347:13

354:19 369:18

stepping 217:11

354:13

steps 128:1

131:8 132:6

172:12

stewards 19:2

stick 237:22

353:10

stimulating

275:9

stingrays 15:7

stockpiling

111:19

stolen 382:12

stood 278:8

stool 345:10

stop 17:22 49:18

77:18,19 105:5

207:4,6 263:4

291:15

storage 152:13

152:13

store 148:18,21

stored 19:8

103:13 221:3

storehouse

293:9

stories 16:17

story 69:2

122:18 351:1

straight 109:15

323:16

strange 197:20

361:17

strangest 59:13

strategic 68:8

68:19 69:3

streamlined

132:17

street 148:19

streets 311:20

strength 376:12

strengthen

225:20 281:7

strengthening

131:9
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

58

stress 246:17

stretched 346:4

strict 6:22

114:11 299:20

strides 234:8

strike 169:20

strikes 30:21

77:12

striking 133:22

134:5 337:8

strive 299:19

strong 24:14

29:17 31:1

111:3,7 115:16

125:6 126:3

196:5 230:6

235:1 268:5

280:18 299:3

369:19 378:22

stronger 191:3

195:10

strongly 29:9

128:10

struck 53:7,13

88:22 89:17

253:5 352:3

structure 21:20

62:11 211:1

240:11

structured

228:4 285:12

structures 258:3

struggle 197:5

struggling 87:11

student 16:18

145:19 229:11

students 157:9

157:10

studied 181:16

studies 160:14

study 145:1

176:10

stuff 120:17

152:20 154:9

174:21 180:10

214:11 334:14

348:15 367:19

370:1

subcommittee

117:7

subject 130:8

137:13 141:18

219:15 221:14

222:15,17,21

281:20 301:1

333:12

submissions

88:6

submit 6:14,16

206:3 255:4

384:4

submitted 7:10

110:5 318:22

383:6

subscriber

169:7 175:14

subsequent

131:16

substance 8:5

substantial

36:12 68:13,22

253:16

substantially

302:1

substitute

348:12

substitutes

288:19

success 36:2

237:9

successful

267:10,12

304:12

sudden 347:11

suddenly 148:9

suffer 60:18

suffered 292:5

suffering 54:6

55:5

suffice 304:3

sufficient 23:18

195:10 281:4

300:5 341:10

sufficiently

253:14

suggest 132:20

195:8 200:9

236:16 276:14

285:21 311:8

381:11

suggested 58:3

199:14 381:15

suggesting

166:21 183:5

288:5 350:21

350:22

suggestion

373:10

suggestions

165:5 305:6,15

suggests 28:18

57:10 108:10

339:20

suit 298:6

299:17 327:7

sum 15:1 105:20

126:16 148:7

summaries

301:22

summary

105:17

summer 120:7

375:2

super 134:11

174:14

superb 287:15

superbrief 76:5

superviseory

223:11

supervisory

219:11

supplement

132:19 354:8

support 56:5

57:11 104:8

130:3 150:14

187:3 237:5

244:4 254:20

312:6 350:4

354:20 366:13

supported 56:1

126:20

supporting

269:21

supports 227:15

231:4 234:14

suppose 42:14

107:7 371:3,17

supposed

272:18 285:18

320:21 368:22

supposedly

302:22

supreme 14:12

65:5,8 71:16

76:8 150:14

292:13 369:9

374:1 375:1

sure 10:19 13:12

18:21,22 41:20

42:13 48:15,18

50:10 61:19

71:3 73:2 76:3

83:8 93:21

94:4,6 96:3

98:10 102:3

107:8 146:2,9

157:3,6 159:2

162:20 168:9

173:7,15 175:3

175:17 185:3

203:13,15,18

208:21 209:21

210:22 211:2

211:19 232:20

237:18 240:16

242:11 246:14

247:12,13

248:5 249:8

251:9,21

252:11 254:21

256:17 259:13

265:11,18

267:3 278:13

289:8,12

303:20 311:13

320:19 340:9

356:11 379:1

surgical 317:5

365:12

surgically 366:1

surpassing

111:2

surprised

165:19 179:8

241:11 242:20

290:3 301:13

surprises 38:2

surprising

301:14 330:8

surrender 98:3

surrendered

99:2 150:21

surrendering

147:19

surrenders

97:16 150:15

surround 348:5

surrounding

238:11

surveillance

5:12 16:16

20:14,17,19

21:12 23:12,14

23:21 24:11,18

32:7 44:14

46:3 52:9,11

67:13,14 72:16

81:1,6,18

84:14 85:5
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

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87:15 127:22

128:11 129:7

130:5,8 148:10

148:17 153:15

167:2 168:13

180:14 186:6

212:11,21

229:19 249:1

295:18 297:12

297:20 298:1

300:13 301:11

302:7,9 306:8

327:1 363:16

373:21 375:19

surveilled 81:4

survellance

272:1

survey 16:12

17:19

surveyed 367:1

survive 25:22

suspect 15:17

329:19

suspended

95:13

suspicion 60:13

suspicious 22:19

sustainment

317:11 360:18

swath 324:15

swaths 282:19

sweeney 195:13

switch 348:16

switches 39:16

synchronized

316:1

synergy 39:6

synthesis 335:6

347:1

synthesized

323:10

synthesizes

324:16

system 61:16

62:11,20 77:3

85:10 112:13

135:8,17 138:4

140:20 155:21

156:11 157:18

158:19 170:5

174:11 183:9

187:14 188:21

190:2,3 191:2

191:7,19 209:7

209:8 220:15

220:16,16

269:18 270:18

270:22 292:4

293:7 295:11

325:1,9 326:6

346:15,18

370:18

systemic 316:6

324:19

systems 37:15

37:19,22 38:9

95:19 112:1

134:17 135:3

135:11,18

136:17 140:4

140:14 142:22

146:2,6,9

157:21 158:3

158:17,20

170:22 173:1

182:9 183:5

185:1 201:16

214:15 230:9

245:14,16

247:14 312:8

312:17 315:17

346:8,8 372:14

372:18

T

tagging 348:9

tailored 260:22

take 23:22 37:2

47:4 52:5 61:9

63:20 64:16,17

68:16 72:6

75:21 104:5

109:2 156:1,13

156:16,20

157:7 163:12

163:17 170:4,4

170:5,6 172:7

177:16 190:7

225:3 227:1

231:6 236:5

239:2 240:22

243:22 247:8

257:17 265:22

268:20 269:1

272:7 273:15

276:11 284:7

297:18 298:11

303:19 316:8

317:17 321:8

323:11 335:1

347:13 362:10

362:11 379:13

takeaways

224:2

taken 4:19 69:6

93:21 126:16

131:8 132:20

169:5 170:18

172:9 205:10

355:2 384:12

takes 123:20

171:21 176:21

342:20 379:6

talismanically

288:11

talk 9:10 54:20

64:15 67:11

92:18 99:11

108:1 119:6,17

139:5 147:2

152:5 166:21

203:17 216:8

218:3 219:22

225:20 238:2

268:13 272:19

276:17 326:14

326:16 330:15

341:12 348:14

382:13,16

talkative 139:4

talked 46:5,8

53:21 54:3,14

60:22 63:22

77:4 142:17

147:10 154:17

161:2 166:18

167:20 169:9

169:11,17

176:1 178:9,13

179:19,22

180:7 240:19

245:10 256:7

268:9 282:1

288:18 310:4

319:1 326:13

348:8 373:1,17

376:7 381:20

talking 21:22

29:3 68:15

84:18 87:13

92:10 94:17

100:1 101:16

119:13 137:14

152:6 159:3

165:2 180:13

185:9 203:16

210:19 216:21

233:16 271:16

272:5,17 275:5

329:5,6,7

340:2 341:18

353:18 366:13

talks 147:17

374:2

tangible 54:6

55:5 225:1

target 35:8,20

62:6 141:11

174:7 228:16

256:14 262:20

targeted 44:13

67:13 82:6

181:3,4 230:1

271:13 301:7

303:6

targeting 279:2

targets 128:12

228:9,13 232:6

task 204:6

tax 13:14 69:9

taxonomy 46:11

49:9,20

teach 311:18

teaching 313:10

team 305:6

322:1

tease 262:12

tech 3:3 110:14

143:10 250:21

technical 36:5

36:17 40:22

41:5,9,10

62:15,17,19

63:6,11 68:5

69:3 90:21

91:5,16 92:1,7

95:19 104:8

106:12,12

111:3 237:4,15

249:14,20

250:18 270:12

279:13 305:10

309:19 351:22

360:4 376:15

378:7,21

technically 91:1

114:11 278:9

354:15,15

techniques

111:1 113:6
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

60

114:19 177:13

232:7 321:13

373:1

technological

25:15,22 40:1

61:21 86:13

90:8 101:2

199:17 227:21

245:11,13

278:1 308:10

340:6 350:1

353:21 379:2

technologies

40:6,14 78:19

105:13 106:4

115:17 143:4

230:8 249:1,2

250:2 303:10

309:11 310:1

318:3 341:3

375:10 381:3

technologist

112:11 115:14

116:2,4,16,18

134:22 160:11

165:4 179:17

193:5 247:17

248:8

technologists

115:22 116:5,7

116:20 137:6,8

144:22 145:6

146:4 154:19

162:10,12

164:18 176:3,7

176:8,11,16,19

237:11 239:6

249:5 316:17

349:11 356:16

357:3 383:10

technology 3:2,5

3:19 6:2 15:7

33:14 39:21

40:9,10 61:12

96:1,3 109:3

109:13 110:17

111:7,8 113:4

113:8 117:5,8

124:14 134:2

135:5,15

137:13 142:12

142:13,18

144:8 146:15

147:8,11,22

148:3,4 149:3

149:16 155:1,6

156:18 158:1

162:2 165:3,11

167:8,11 170:2

174:13,22

201:16 206:16

226:16,18

228:10,22

234:2,9,14,19

234:22 247:17

247:19 248:2,4

248:11,14

250:14,16

251:5 252:10

269:21 277:19

285:16 295:16

296:8 304:12

305:14 306:2,7

307:14 309:20

311:13,19

314:4 315:11

315:18,19

320:10 325:2

326:1 334:5

340:20 344:14

344:15,19,19

348:2,3 349:8

357:9 364:6,9

364:9 373:14

373:17 374:18

374:20 375:5,5

377:10,21,22

378:9,15

379:10,20

380:8,11,13,17

380:19,22

381:1 383:14

technologys

380:21

teenager 68:22

telephone 15:15

102:12,21

103:16 120:7

163:17

television 22:12

tell 98:17,18

123:2 166:20

180:6 184:18

261:15,19

262:3 274:20

274:22 294:6

312:9 314:14

321:5 335:11

343:20 367:3,4

telling 15:13,18

99:13 293:21

303:17

tells 294:4

template 232:14

ten 94:9 167:10

181:22 205:20

288:6

tenants 218:10

tend 66:20

77:16 267:19

tendency 61:1

tends 233:18

310:19

tenet 304:11

tension 161:7

tenth 314:12,15

334:17

terabytes

349:21

term 95:13 97:2

152:4 222:2

260:2,3

termed 54:1

terminology

180:3

terms 34:1 58:2

62:8 78:4

92:14 106:3

143:20 153:2

153:14 154:7,8

157:18 159:9

160:3 161:19

164:17 168:16

184:15 185:22

218:18 223:1

245:15 248:20

252:9 261:3

266:5 277:13

280:8 284:12

291:18,18

315:6 319:19

336:9 339:19

342:11 344:5

348:9 356:17

358:5 359:2,16

361:4 373:8,14

terrible 368:21

terribly 289:9

territorial

220:19

terrorism 111:9

133:6 181:21

182:4 183:4

217:2 296:3

365:16 371:15

terrorismrelat...

220:17

terrorist 15:18

161:16 181:18

349:4

terrorists 36:1,4

110:22 111:17

113:1 166:2,15

tesla 26:6

105:11 106:2

test 147:15

198:6 369:15

testify 33:18

110:20 134:21

303:21

testimony 83:2

116:15 118:19

119:6,17 120:3

121:7 147:1

342:15

testing 231:13

252:13

tests 66:13

text 12:19 136:7

136:7

thank 6:18 7:13

7:20 10:11

18:1 25:3,9,10

33:9 41:13,15

55:6 67:17

74:10,14 80:15

80:15 87:6

88:14 96:8,8

102:1 108:19

109:5,8 116:9

116:13,14

117:1,2,9,10

124:9,10,15,15

134:9 141:15

141:16 142:21

159:14,19

165:13 171:9

171:10 172:5

196:7 202:20

207:1,2,7,17

215:8 216:5,7

216:7 225:8

226:4,4 237:17

237:20 263:9

271:4,5 278:2

278:4,4 284:14

285:9,10 287:8

293:16 295:12

295:13,19,21

296:1 303:7,8
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

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303:15,16

313:3,8 318:7

322:13 326:7

328:16 347:16

347:19,19

363:1 374:5

381:6 383:1

384:14

thankfully

193:21

thanks 10:9

33:17 67:21

107:19 108:22

110:19 134:11

134:20 159:19

169:8 284:20

284:22 303:14

313:2,6 383:3

thatd 160:12

thats 8:7 11:18

12:2 13:20

14:5 15:10

18:14 27:14,15

27:16,18 29:8

33:1 46:18

48:6,16 49:10

55:20 60:11,14

60:17 63:13

64:21 65:16

66:4,22 70:4

70:13 71:11

72:4,20 76:16

78:7 80:21

82:13,18 85:4

85:19 87:14

88:5,12 91:13

99:7,8 102:16

103:20 104:2

105:8 106:22

107:19 113:18

118:4,8 121:1

127:21 128:6

132:13 137:11

138:19 140:14

143:9,10

147:14 151:17

151:21 152:8

159:8,21 161:7

161:10,11,12

161:17 162:1,2

166:12 167:6

169:6,22

172:12,15,15

172:16 174:14

174:16 177:11

177:12 178:4

179:18 181:6

184:5,14 185:1

187:2 188:6,14

189:6,14,19

195:22 198:4

199:6 200:17

206:9 210:14

218:12 221:6

222:9,15,20

223:6 224:19

238:9 240:3

241:2 245:22

247:8 248:14

250:12 251:18

252:12 253:3

254:13 255:13

256:6 258:2,16

258:20 259:11

259:20 262:7

265:15 272:6

274:14 275:1,2

275:14 277:10

279:5 281:20

283:8 292:18

293:15 304:16

306:4 307:16

311:19 314:2

315:3 320:8

321:11,14

322:19,20

326:21 330:10

334:14 336:11

338:2 348:11

349:1 350:3

353:5,6 354:2

354:5,21 356:7

357:13,20

358:13 360:15

363:4 366:16

366:21 368:11

369:8 379:22

theme 211:15

theoretical

285:14

theoretically

115:18

theory 38:15

76:7 78:5 89:1

89:3,11,12,18

97:5 250:12

268:13 368:5

383:14

thered 306:18

theres 10:14

12:6 13:12,13

14:7 21:13

23:3 40:17

42:18 43:18

44:2 48:5,8,17

58:9 62:15

64:4,13 71:12

75:6 78:21

79:11 83:21

84:5,8 90:16

91:15,22 96:4

98:19,20 100:3

103:10,11,17

103:18 104:13

108:14 118:11

119:3 121:17

128:2 130:22

133:18 137:17

144:19 147:6

149:17 150:13

151:14,14

152:2,7 153:4

153:19 154:9

156:15 164:7

171:1 172:7

173:6,10 175:6

177:6 183:9

184:19 185:19

192:22 193:3

193:13 197:12

198:12,16

207:4 211:5

220:2 221:18

253:9 258:4,5

258:11 273:19

281:16 292:1

292:21,22

293:1,2,2

308:3 317:1,2

324:13 328:4

329:1 332:12

337:1 339:22

344:13 346:16

349:18 351:13

353:15 357:16

363:6 364:5

371:21

theyll 48:2

88:10

theyre 8:16 17:1

19:18 22:12

24:18 30:2,6

41:8 45:17,17

54:21 63:6,7

77:18 88:11

100:10,11

107:15 126:11

138:12,13,21

141:4 147:18

158:9 168:2

174:8,10,11

177:19 182:10

187:10 188:8

188:10 191:12

191:13,15,16

191:18 199:1,2

199:2 224:22

251:3,12

267:17 283:16

283:16 287:1

310:7 311:18

312:1 315:13

326:19 328:9

332:19 334:17

334:18 342:3

343:2,14,16

344:6 357:6,7

357:8 363:15

367:6 370:8

379:1

theyve 24:10

33:20 106:10

149:14 177:5,7

289:8,10 367:2

371:6

thin 79:12

thing 10:14 14:3

18:18 21:11

46:9 51:4 57:9

57:15 71:8,11

78:12 82:16

91:14 93:6

94:12 141:2

152:13 156:1,7

156:10,13

166:16 176:15

198:19 201:13

201:15 213:17

242:22 259:16

261:18 262:3

262:10 268:6

270:17 274:10

276:19 305:1

311:1 321:14

322:20 331:1

343:4 351:5

354:16 355:19

366:11

things 10:17,19

11:21 18:16,19
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

62

19:5 21:8,13

25:1 28:12

29:15 30:14

32:17 42:22

43:1 44:11

46:5 49:17

50:3 52:3 53:6

59:13,15 68:9

68:9,13 71:6

84:4 86:9 93:7

94:10 95:18

104:13,14

105:3 115:1

128:4 136:18

138:1,8,9,10

138:11 139:1,1

139:16 143:11

143:18 144:2

152:11,12,22

154:14 155:2

156:19 157:4,6

164:8 170:10

170:21 171:2,7

173:6,16 183:4

184:11 185:17

186:3,5 189:21

190:22 191:9

191:16 199:12

202:6,17

247:16 250:13

251:6,15

252:19 257:10

263:4 266:2

267:21 270:18

271:1 272:15

274:13 279:15

290:13 303:13

304:19 305:4

308:15,18

309:8,18,21

312:3,15 315:7

316:4 319:2

321:16 324:4

329:8 330:9

334:9 335:3,13

336:4 339:4,16

349:14 351:16

356:17,18

357:5 359:5

361:15 379:3,8

381:19

thingsternet

139:2

think 7:21 10:3

10:21 11:8,8

14:10 17:16

19:9,19 20:10

21:18 25:19

26:3,5,10,12

26:15,16 28:19

28:19 29:6

30:9,14 32:3

32:11,16,18

33:21,22 36:20

40:4 42:10,18

43:18 44:2,3,4

44:21 45:19

47:17,21 48:6

48:6,16 49:20

49:21 50:3

51:12 52:1,3

52:21,22 54:1

55:7 57:8,12

57:17,19 58:20

59:9,13 60:1,7

61:3,15,19

62:8 63:14,21

64:20 65:9

67:8,12,21

68:14 69:5

70:2 71:15,18

71:20 72:8,14

73:5,8,9,20

74:5,6,7,8,12

76:6,11 77:9

77:16 79:3,14

81:2,14 82:20

83:1,5,10,11

83:13,14,17,19

83:20 85:2

86:8,9 87:9

88:18 89:10,16

90:1,20 92:8

93:5 94:12

96:4,6,9,13,20

97:1,11 98:15

99:11,22 100:6

101:1,5,13,19

102:18,20

103:3,21

104:12,13,16

105:3,4,8,9,11

106:3,15

107:20 108:11

108:13 119:3

119:21 120:4

120:12,17,19

141:19 142:2,8

143:21 144:13

144:19 147:2,7

147:21 148:1,2

148:11 149:3,7

149:12,13

150:12 152:5

155:9 157:3,8

159:5 160:20

162:1,2,9

163:8,11,12,18

164:11 166:5

166:15,16

167:11,13,22

175:8,21 177:1

177:4,8,9

178:19,22

179:2,4,17,19

181:6 182:21

184:15 185:6

187:4,7 188:18

190:2,7,11,18

191:6,6,7,7,12

192:21 193:2,9

195:19 196:3,5

196:15 197:11

197:15,20

198:8,15

199:14,19

200:2,13,17

205:1 206:15

207:20 208:6

210:15,18,20

211:14,17,21

212:3 213:3,5

213:14 220:1,6

222:9 225:13

225:14 227:20

233:20,22

235:20 236:1

241:1,19

244:11,12,19

245:15,22

247:4 248:8,9

249:19 250:20

251:17 252:14

253:9,10 254:4

254:8,14

257:21 258:20

260:3,4,6,12

261:19,21

263:2,15

264:14,17

265:15 266:3

266:15 267:4,5

268:6,6 269:12

270:3,10 274:3

274:12,16

275:14 276:6

276:12 277:1,3

277:10,18

278:13 281:6,9

282:4,11,12,17

283:2,4,5,6,11

283:21 287:1,9

287:17 288:3

288:10 289:5

290:1,10,12

291:9,12,20

292:1,15,18

293:11,13,18

294:1,11 295:1

295:2 305:16

306:6 309:16

311:10 313:14

314:2,4 315:3

317:14 319:6

319:21,22

321:11,21,22

327:9,21

328:19,21

329:3 330:4,17

331:5,9,14

332:8,10 333:5

333:6 334:2,4

334:15,18,21

335:5,8 336:5

336:7,8,11,15

336:18,19

337:1,3 338:4

338:12,13,15

340:2,16,21

341:3,13 344:4

344:6 345:3,21

346:2,6 347:16

348:7,16 350:3

351:13 352:3

352:15 353:9

353:11 354:18

354:21 355:10

355:14,16

356:1,2,7,16

358:18,20

359:10,14,17

360:2 361:7

363:9 364:19

364:21 366:7,9

366:14 367:13

367:18 368:6,7

368:8 369:22

370:1,10

371:21 372:3,9

372:10,13,22
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

63

373:4,6 374:6

374:7,9,14,15

374:20 375:6

375:19,22

376:9,13 377:5

377:7,11,16,17

378:4,6,8,13

379:4,22 380:3

380:4,10,12

381:9,14

382:18 383:7

383:17

thinking 26:6,10

67:22 69:22

81:15 86:11

101:14 143:17

143:18 210:19

229:6 250:4

292:16 295:8

308:9 312:2,15

320:1 331:5

333:22 338:13

352:16 355:1

thinks 179:9,9

229:4

thinner 45:1,1

third 15:11

20:12 34:3

35:4 39:6 91:2

111:13 119:17

130:3 150:16

151:5,17

168:16 229:4

283:14 285:17

291:22 302:3

315:15 345:9

thirdly 97:11

thirdparty

13:21 72:4

97:13 197:13

353:15

thirty 32:13

thomas 136:19

thorough

111:12

thought 32:14

51:4 54:3

55:14 83:4

84:10 87:18

90:7 164:15,16

165:6 184:4

199:16 202:11

216:22 229:11

256:1 261:10

273:2 276:22

319:17 330:17

336:1 344:10

351:7,8 365:10

thoughtful

383:8

thoughts 82:22

87:8 153:4

183:7,18

187:21 189:7

192:17,21

227:4 235:9

257:13 259:22

268:17 322:14

328:3,13,17

366:10

threads 316:6

threat 66:8 67:3

94:17 111:13

114:15,19

115:12,19

199:20 217:13

218:15 226:14

227:20 228:1

threatens

302:19

threats 115:8

133:4,7 227:11

228:6

three 34:1 91:10

111:6 123:14

141:7 205:16

205:19 236:14

263:1 315:2,8

322:10 331:19

346:20 360:16

360:19

threshold

276:15 322:22

thresholds

272:15,15,19

335:7

throw 44:18

63:20 275:17

throwaway

138:18

throwing

307:11

thunderbird

26:2 40:5

tight 159:12

tightly 267:9

time 6:16 9:13

9:20 15:2 17:6

17:7 18:11

30:4 33:3

41:13 49:14,18

60:7 72:14

76:4 80:13

84:2 87:21

113:15 116:21

120:2 123:7,9

135:22 136:16

137:18 138:17

139:6 141:18

144:9 146:1,11

148:13 164:1

164:21 168:1

175:9 190:8

192:11 196:11

205:21 206:2

214:22 215:1

218:4 220:21

223:22 226:8

227:1 231:19

233:16 239:7

242:9 252:15

255:7 275:5

276:13 280:6

286:7 287:9

297:10 310:20

310:22 313:9

319:14 320:15

322:14,20

325:3 333:22

334:5 335:10

336:3 342:20

344:17 346:3,7

347:7,15,17,21

348:4 360:11

360:18 362:12

362:20 363:9

364:7 367:20

368:1,18

370:11 373:19

379:6 380:17

381:7

timekeeper 6:22

109:21

times 39:12

71:18 83:6

110:2 145:3

149:2 159:5

190:16 241:10

257:4 273:12

295:8 343:7

363:5

timewise 75:22

tinker 196:2

tiny 120:10

tipping 256:19

260:2

tips 260:8,9

tire 303:17

title 109:11

219:9 247:3

today 5:17,20

6:19 9:3 25:12

26:3,7 33:18

59:14 61:4

92:10 98:6

105:12 111:6

117:11 123:5

124:16 125:22

134:7,21 151:7

181:22 205:1

205:16 216:21

227:1 234:7

235:20 264:3

269:8 283:10

285:3 295:22

303:16 305:13

306:8 307:14

307:14 309:8

309:14 333:12

347:21 363:5

todays 5:15 7:6

7:17 10:13

33:22 35:10

123:21 340:20

383:22

toes 255:6

token 143:8

184:16 204:14

told 172:20

257:4

ton 178:1,1

tool 195:14

289:2 291:1

339:11 381:16

tools 117:15,16

126:12 133:5

143:16 173:8

175:1 177:13

190:12 233:9

234:9 314:7

320:11 349:11

382:9

top 81:7 82:2

150:4,5 162:7

181:11 272:18

296:13 349:5,6

topic 207:22

216:9 341:17

topics 16:15

383:11
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

64

total 15:1 71:5

148:7

totally 17:18

52:2 298:9

350:7,8

touch 127:8

252:11

touched 170:10

288:2

touching 139:9

139:11

touchstone

69:17 363:20

toughest 327:2

touted 183:11

183:17

town 296:5

track 71:16

280:6

tracking 306:19

tracks 222:18

trade 216:12,15

314:1

tradecraft

187:17

traditional

204:9 232:4

274:8

traffic 39:10

trail 276:18

trails 93:3 94:4

319:2

train 174:5

214:15

training 94:22

127:5 172:12

172:14 173:20

215:3 244:15

378:4

transaction

331:7

transactional

152:1 320:4

transactions

248:2

transborder

153:8,15,15

154:16

transcript 7:7

384:7 385:8

transferred

90:17

transiting 255:1

transition 60:20

88:20 107:22

transitions

101:2

translate 88:19

305:11 340:18

translating

171:14

translator

378:14

transmitted

139:9

transparant

208:13

transparency

23:2,10 28:5

31:13,14,17,18

31:20,22 32:4

32:9 84:5 85:8

85:15 86:2

89:4 125:22

128:22,22

130:11 152:16

163:8 164:17

165:10 204:16

207:12 209:6

210:17 211:9

213:15 214:2

214:13 215:2

220:7 271:19

272:3 274:8

280:14 298:16

301:10 302:1,4

303:7 327:16

337:18,19

345:9,10 356:5

transparent

89:5 208:18

214:6 298:19

transperancy

212:1

transportation

59:20,22 60:5

trash 307:11

travel 9:5

treat 121:18

treated 150:8

231:7

treaties 51:8,13

tremendous

142:19

tremendously

288:4

trend 142:4

185:15,19

trends 144:16

186:7 187:22

tribal 220:19

tried 36:1 85:12

137:19

trigger 60:13

trillion 141:7

troops 227:15

troubling 80:12

true 17:9 48:6

99:7 130:13

151:21 336:19

truly 40:20,21

trump 66:14

67:9

trust 24:10 58:1

58:4 85:15

115:12 125:7

125:11,12,14

127:12,17,20

128:5 135:16

247:12 249:11

265:13 266:13

300:7

trusted 242:10

trustworthy

155:17 234:11

truth 32:8 99:13

361:17

try 45:10 56:22

61:20 62:7,19

68:2,6,6

105:21 160:17

165:15 173:14

175:1 178:16

249:19 262:12

269:13 316:9

323:16 340:21

348:13 361:13

378:15

trying 27:10

68:14 72:21

82:14 84:22

159:9 161:16

171:5 175:5

191:18 196:1

199:17 213:7,9

239:8 242:3

243:6 247:10

251:14 256:2

263:15,21

264:10,12

275:3 289:3,5

291:15 313:21

315:17 316:19

320:2,3 328:22

334:3 342:12

343:14 344:5

345:7 376:11

378:18

tsa 58:14 59:16

83:13

tssi 193:8,15,16

193:22 201:6

tuning 182:18

turn 7:18 28:18

34:21 36:22

37:7 61:22

109:16 203:17

252:15 284:10

284:10 326:7

turned 82:8

122:21 202:12

205:18 321:2

turning 215:9

turns 250:14

252:7 322:8

tweeted 339:9,9

twentyfirst

337:17

twice 70:9

twist 337:16

twitter 136:21

367:22

twitters 368:4

two 9:12 30:15

34:14 43:22

58:13 76:17

91:9 105:9,17

110:1 114:17

116:20 119:13

122:4 124:21

140:7 186:2

187:22 194:6

199:10 211:15

217:22 250:17

274:4 276:1

278:8,11

279:11 286:5

306:1 309:5

314:6 326:10

334:9 336:4

345:13 361:2

361:18 362:1

371:4 372:19

380:9

type 31:16

129:21 168:11

239:5 248:18

251:10 262:17

302:10 331:16

370:19
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

65

types 45:21

46:12 56:11

85:7 103:11

169:7 219:12

230:5,5 235:17

235:21 236:4

236:11 268:10

typical 70:15

323:1,10

325:15

typically 265:3

298:10 315:16

374:15

typo 137:3

U

u 3:13 36:4

127:19,20

128:4 129:10

129:14 131:15

131:17 132:1

132:12 133:2

164:5 230:2

278:22 279:2,3

279:21 289:9

292:4 301:8

302:10 343:17

369:7

uavs 78:20,21

ubiquitous

50:18 147:19

148:16 186:5

379:10

ultimately 23:13

74:8 226:19

240:10 316:13

316:20 323:8,9

372:15,18

umbrella 97:2

unabated

364:11

unable 370:20

unacceptable

271:1

unanimity 97:12

unanimous

133:15

unavoidable

360:7

unbelievably

291:1

unbiased 378:15

unclear 291:7

uncomfortable

269:4 363:13

363:16 378:22

unconcerned

45:6

undercut 377:2

underlie 55:22

285:15

underlies 28:16

underlying

28:21 56:6

57:11 84:22

85:3 101:14

243:20 333:18

undermined

127:12

underneath

270:1

underpinning

274:14

underpinnings

285:14

understand

29:1 36:19

37:20 38:1

89:11,12,13

157:12 158:2

165:21,22

178:6 183:14

195:22 235:3

242:14 245:5

245:14 247:14

249:11,17,18

252:11 279:12

279:13 294:9

328:10 353:8

355:2 360:4

367:21 368:2,4

379:7 380:13

understandable

375:11

understanding

107:9 129:21

239:7 242:2

244:22 249:1,2

251:10 264:4

279:1 291:4

340:4 344:8

377:9 380:8

understands

328:8

understood

196:10

underwent

30:20

undesirable

40:16

undue 362:8

unduly 8:8

14:10

unencrypted

162:4

unfolded 122:19

unfortunately

44:13 62:16

unfortunatly

205:17

unique 34:10,16

39:9 234:10

uniquely 205:1

unit 240:3

281:15

united 51:8

127:22 193:8

343:22

units 228:5

universal 51:6

98:7

universe 298:4

universities

144:4

university 2:12

2:15 3:3,18

110:14 117:5

287:3

unjust 310:22

unknown 90:5

unlocks 37:7

unpleasant 38:2

unpopular

140:18 268:1

unreasonable

42:3 64:13

72:10,13,17

369:1,4

unremarkable

36:14

unrestrained

298:3

unscientific

17:19

unsolicited

358:15

unsurprising

315:20

untenable 32:11

unusual 186:3

unwilling

108:11

update 168:12

updates 374:12

upfront 358:9

uphill 243:6

uploaded

139:21 140:2

upset 265:22

upsets 302:21

urge 124:8

128:17,19

130:14 377:3

urges 303:1

usa 212:6

usable 138:16

usage 38:21

322:12

use 18:20 27:15

38:14,14 39:10

40:13,15 46:5

52:3 55:13

61:12 63:4,10

68:5 69:1

77:14 78:19

82:17 86:11

87:20 91:17

94:8 106:5,9

106:11 113:1

115:16 118:17

121:10,13,22

122:12 123:2

123:19 124:3

125:10 126:1,3

126:9 131:9

145:19 150:22

152:9 155:12

157:11 162:6

169:21 170:12

170:13 171:3

174:11,13

184:10 188:2

198:21 222:9

222:15 226:20

231:18 236:4

236:11 237:1

248:16 257:22

267:10 274:17

276:7 288:10

288:13 290:20

291:10 292:2,6

292:17 294:21

298:11 300:10

305:22 306:3

308:15 309:22

310:12 312:8

312:15 315:19

320:10 321:9

321:12 323:12

323:13 324:14
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

66

324:19 329:7

338:3,5,6

341:9,18 342:1

342:6,14

344:16 350:10

351:10 363:17

364:8 365:11

365:15 372:12

372:21 380:22

381:2,20

382:16,16

383:12

useful 62:1

114:6 115:10

118:8 119:4

134:5 195:14

195:16 259:14

259:15 273:21

274:1 276:3

288:4 289:22

293:20 296:15

296:17 332:1

332:10 333:5

344:22 366:16

367:4 368:15

381:16

user 39:15,19

79:17 201:21

users 39:11,16

39:20 112:16

156:8 157:7

237:1

uses 15:6 35:4

71:9 121:15

214:19 222:17

234:15 236:7

236:12,13

262:21,22

294:13 322:9

362:7

usp 164:10

usually 39:19

147:16 229:22

utilitarian 26:22

29:14 50:22

51:4 68:15

72:9

utility 367:11

utilizes 204:20

uttered 66:14

V

v 133:14

vaguely 269:16

vagueness

157:17

valid 97:20

260:19

validated 227:8

valley 135:1

136:13

valuable 27:5

93:8 195:6

197:12 198:5

207:14 291:1

361:6

value 9:2 11:4,6

15:19 17:20

26:11,16,20,22

28:15,21 29:14

50:19 51:5

54:9 55:12

64:4 69:13

77:6,7 85:3

98:21 116:19

117:19,22,22

125:5 132:21

133:16 144:8

214:1,4 222:1

233:1 244:11

265:19 266:6

266:10,17,20

270:10,11

284:11 291:13

312:5 331:2

358:8 381:20

values 8:5,15,17

26:21 27:8,22

55:22 56:1,6

57:12 67:6,11

84:22 97:3

118:5 209:3

234:19 331:17

357:18

varied 9:4 180:3

variety 50:16

230:16 237:10

246:2 247:15

304:8 308:5

various 46:12

76:12 104:14

105:3 163:22

241:7 255:19

325:18,20

346:1,21 373:1

vary 95:8

varying 8:16

196:11

vast 21:14 53:14

64:20

vehicle 40:7

velocity 50:16

venture 313:5

venue 313:13

verify 247:13

verizon 293:3

version 75:17

88:3 100:3,4

326:17

versus 114:11

336:5 369:7

vetting 263:16

263:22

viable 327:5

victims 113:22

183:15

video 139:20,21

140:2 167:2

329:16

view 13:21

32:10,11 33:15

63:2 81:12

98:1,1 132:2

151:22 171:14

192:6 210:12

212:12 214:1

241:1 296:18

299:11 322:15

324:20 339:21

340:10,11

342:9 351:6

376:8

viewing 338:17

370:1

views 19:11 54:8

96:17 231:15

284:19 333:17

vigorously

154:4 339:5

village 76:21

violated 29:10

violation 15:10

281:4,19

violations 15:11

16:22 281:10

281:11 327:7

violaton 222:20

violence 160:4

161:22 270:6

virtually 30:6

virtue 14:2,15

visavis 11:1 45:8

188:10,12

vital 214:20

379:5

vivid 36:15

vocal 127:21

voice 10:11

243:3 244:17

284:4

void 51:14

volume 37:11

50:15 129:21

228:19 280:9

289:14

volumonous

269:9

voluntarily

219:5

voluntary 273:6

volunteered

122:8,9

vote 4:19 384:12

voyeurism

31:21

vulnerabilities

49:11

vulnerability

16:6

vulnerable

121:12 123:19

W

w 1:13

wade 257:20

wait 115:18

201:9 353:18

372:11

waived 122:14

wald 2:5 4:15

7:19,20 10:10

18:1 25:4,9

33:8,11 41:15

43:8 45:10

50:10 51:2,15

51:22 53:2,4

53:20 57:14

60:21 62:22

63:19 67:17

69:4 71:2

74:10 75:21

80:14,18 87:7

88:5 96:6

102:2 103:9,21

105:9 106:14

106:18 107:3

108:16,20

175:20 177:18

178:7 179:12

179:19 180:16
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

67

181:3,6 199:9

200:19 201:1,4

201:9 202:8,20

255:8,12 258:7

259:4,7,10,13

372:8,9 374:6

377:5,14

walk 304:21

379:20

walking 14:2

273:21

want 6:18,21

7:13 20:3,4,4,9

21:9 22:7 24:6

46:15,18 47:20

47:20 48:7

51:3 53:8

63:21 64:8

69:20 76:16

83:1,12 86:15

87:17,19,22

88:4,9 91:1

93:14 98:15

99:3 104:16

108:1 115:2

120:3,13 133:4

143:21 146:21

147:1 156:9

158:22 160:4

169:9 181:14

185:10 189:9

192:5 194:2

198:3 202:2

206:22 207:1,7

207:17 215:5

235:20 239:2

243:22 248:11

250:7 257:12

263:4 265:5,18

267:11 269:3

269:18 270:17

270:21 271:8

273:15 275:16

275:21 278:6

279:6,8 283:19

287:16,21

297:3,11 306:5

309:4 333:13

335:9 344:1

354:3 356:18

360:14 367:22

368:2,4 374:4

374:16 379:15

380:15 382:4,9

wanted 46:13

47:15 61:16

87:14 89:19

102:10 142:1

180:16 201:10

218:3 220:14

237:22 262:9

263:12 273:13

305:13

wanting 329:20

wants 63:3 73:3

75:21 106:8

264:22 367:18

379:19

war 122:6,7,15

122:18 187:6

228:2

warning 110:2

warrant 29:18

53:10,16 54:7

65:11,13 70:18

71:21 73:1

81:21 132:11

167:22 168:6

168:10,21

169:5

warranted

366:2

warrants 42:4

70:11

wartime 123:11

washington

1:13 2:15 4:7,8

18:4 130:19

197:1 363:12

wasnt 65:11

181:18 199:22

307:3 358:3,3

358:5,6

wasted 68:18

watch 183:22

198:16 369:17

watched 280:12

watchers 21:13

wave 287:11

way 8:10 11:16

17:1 19:3

21:16 28:2,19

31:10 32:9

41:2 56:5,9

57:11 61:14

62:12 63:7

64:21 67:8

78:6 97:22

98:1,6 113:9

115:4 121:18

124:6 125:18

144:12 145:4

145:10 150:11

164:17 172:22

173:2,11 175:6

175:22 183:6

190:2 191:20

192:4 197:6

198:16 200:4

200:16 201:17

201:21 202:6

202:13 209:1

210:21 211:5

224:19 229:6

230:12 242:5

258:12 260:6

260:12 261:3,9

265:12 271:20

276:7 283:20

285:12 287:11

289:7,22

294:22 304:14

307:20 309:7

327:4 328:22

329:21 330:4,5

332:5 334:21

335:2,8 336:11

336:14 337:12

338:7 345:3

347:22 348:3

351:7,14

352:20 353:20

354:21 355:10

355:22 356:9

358:14 359:9

364:9 373:22

374:4 377:12

377:15 378:15

380:5 381:16

382:5,19

385:10

ways 16:21 21:5

21:17 24:3

26:9 49:19

68:1,16 69:16

74:18,21 89:6

99:15 116:11

147:16 148:11

149:14 163:9

164:11 165:9

184:16 191:15

199:5 214:3

215:2 240:17

240:18 244:20

258:4 261:2

265:14,20

277:20 279:7

280:17 295:7

307:22 319:5

334:6 337:17

339:3 350:12

371:4 373:13

374:21 379:20

weak 24:11

weaken 111:21

weakening

111:20

weakens 166:1

weakness 292:3

weaknesses

342:16

weapon 111:5

weapons 56:22

227:13 365:17

wearables

185:17

wearing 164:22

web 220:10

website 7:8 39:9

384:8

wed 146:5,6

176:13 318:4

330:20

weeds 218:5

week 7:8 120:15

157:8,9 224:13

373:19

weekly 184:20

weigh 52:5,22

53:6 66:18,20

67:1,5

weighing 33:5

232:22

welcome 4:2

7:10 134:18

162:11 181:13

203:9,10 225:7

237:14 375:22

376:1

welldefined

155:2 158:13

wellunderstood

14:6

wellwritten

351:19

went 45:13

80:19 160:16

161:11 175:3

175:12 304:20

315:2 374:11
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

68

westend 4:6

weve 15:12 32:9

51:13 58:6

92:3 95:18

126:16 127:4

127:12,21

128:4 129:4,13

129:18 131:7

132:11,15

150:3 154:17

168:4 169:5

172:9 173:6

203:13,19

229:5 232:13

235:19 262:11

262:14 263:1

269:2 284:3

285:12 320:18

331:13 337:16

337:17 341:18

343:9 349:9

350:22 366:21

367:1 373:1,17

383:7,8,11

whack 63:20

64:16

whacks 64:18

whatnot 247:10

whats 23:7

24:21,22 25:1

28:14 51:15

75:17 76:6

78:4 81:12

84:9 93:12

98:11 123:18

126:16 160:8

176:14 177:15

192:18 244:22

250:21 251:5

256:19 326:4

330:21,21

364:21 379:21

380:2

whatsoever

292:7

wherewithal

195:3

whispers 164:19

wholesale

108:12

wholl 367:20

wholly 315:10

316:11

whos 60:4 175:4

196:1 220:10

250:19 256:14

285:1 328:18

wide 282:19

319:1 364:12

widely 204:8

283:3 337:15

willful 95:9

222:11

willing 8:16

13:18 41:11

85:17 87:13

101:5 106:9,10

282:12

willingness 95:6

willynilly

107:14

wilson 7:14

wind 346:17

winn 6:15 7:16

winning 8:6

wins 19:18

wiretap 166:11

wish 135:5

177:21

withhold 14:9

withstand 101:2

112:14

witness 117:3

124:11 385:12

witnesses 96:9

96:19 99:21

109:16,19

woman 37:8

women 35:21

270:6

won 278:14

wonderful 99:8

143:6 167:6

wondering 55:1

82:11 259:21

271:21

wont 15:20,21

105:21 191:17

366:15,19

word 27:7 31:12

71:2 248:15

words 66:14

108:18 211:8

218:11 240:11

256:20 257:3

262:18 329:13

330:8 331:2

350:20 355:11

work 38:1 62:19

88:21 91:15

95:3 99:15

100:15,15

132:22 133:2

140:6 143:9,9

145:17 148:19

148:20 154:10

182:10 189:14

189:19 195:20

200:14 205:15

207:10 217:6

224:7 234:12

238:17 240:5

241:3 246:7,11

246:20 255:14

270:1 273:15

273:17 274:15

283:15 284:8

289:12 296:1

308:11 314:3

317:11 319:2

319:13 327:1

327:12 328:7

328:14 329:14

349:19 356:17

356:18 371:20

373:19

worked 127:12

136:5 156:18

176:17 226:1

289:9 303:18

worker 59:22

workforce 45:4

171:19,20

172:11 214:16

working 12:11

144:3 145:20

205:9 213:16

239:6 242:12

242:17 250:3

250:20,22

251:12,13

252:5 275:8

283:12 311:7

312:18 347:5

workings 129:7

works 54:12

83:10 89:14

153:16 189:18

227:14 251:22

251:22 295:11

304:5 356:10

world 13:10,19

28:12 32:12

55:15 110:21

122:7,18

131:21 132:2

133:9 135:19

135:21 136:16

136:20 137:5,5

137:22 140:9

153:9 158:12

166:5 187:6

190:4,5,9,15

191:9,16 198:1

237:8 250:22

289:13 304:8

312:16 331:8

331:12 335:19

339:11 356:22

361:17,19

363:18

worried 178:3

258:13 266:18

304:22

worry 15:14

18:10 22:17,18

43:15 68:11

120:16 147:7

161:13 176:7

349:9

worrying 20:10

worse 138:6

worst 13:20

16:21

worth 26:22

227:6

worthless

117:20

wouldnt 64:5

82:19 149:18

180:11 202:9

307:6 318:15

355:18 380:15

381:4

wrap 9:13 96:10

134:8

wrapped 59:15

wrapping 207:5

wrestling

368:17

writ 66:20 67:5

67:6 333:9

write 88:10

136:20 145:9

286:18 318:11

378:18

writer 102:9

104:5

writers 16:13

writing 16:15
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

69

83:2 312:12

writings 61:4

written 6:14 7:9

9:20 30:15

64:3 118:19

119:6 121:7

144:6 145:4

146:22 195:13

223:13 240:21

276:2 318:10

318:22 319:3

326:13 328:5

328:18 384:4

wrong 71:16

93:16 107:16

166:20 193:9

294:20 322:4

328:20 330:7

331:21 353:9

369:22

wrongdoing

281:2

wrongful

222:21

wrongly 52:4

wrote 45:12

46:8 119:12

288:7

X

x 183:22,22

184:1,3,4

363:14

xyz 202:1

Y

y 363:14

yall 238:1,2

yeah 136:9,11

147:14 157:11

162:20 168:3

168:22 187:21

188:1 200:20

281:9 339:12

year 7:12 128:2

129:10,15

130:19 217:20

232:14 284:3

384:6

years 10:16 94:9

123:14 124:13

125:2 128:6

137:15,16

149:6,19

153:20 157:3

163:13 167:10

177:2,2 181:22

227:19 229:9

230:15 288:6

306:12 311:20

322:9,10

360:18 361:2

379:13

yellow 9:11

110:1 207:5

286:4,5 293:15

293:17 370:12

yep 277:6,8

yesterdays

123:22

yield 112:13

189:8

york 241:10

youd 255:4

307:2 318:9

youll 41:4 69:1

69:2 177:2

271:7 286:5

287:11 293:17

335:16

young 17:7,8,9

17:19

younger 338:16

youre 68:14

69:18 77:19

83:13 99:12

100:18 150:19

152:13,22

166:21 182:14

194:2 199:7

212:19 220:2

241:21 258:13

259:2 260:8,13

261:20 265:18

265:19 270:2

272:17 273:8

276:14 277:22

279:2 283:5

287:10 305:1

310:15 312:10

315:17 318:14

320:12 323:17

332:3 338:15

347:9,11

348:16 349:16

349:20 350:10

351:2 353:18

357:18,20

367:4 371:12

378:9

youtube 139:21

139:22 140:2

youve 16:4

30:15 61:3

105:15 108:20

120:14 149:7

248:22 249:22

273:1 289:4

297:10 313:13

322:1 353:9

354:10 383:16

Z

z 363:14

zero 151:3

zeroday 111:19

zones 40:9

zoom 170:19,19

170:19

0

0 303:19

1

1 2:9 7:5,20

203:1 352:11

10 109:2,3

284:17 379:13

385:18

100 352:8,11

11 217:12

12 1:7

1200s 76:22

1221 1:13

12333 82:6

164:7,10

229:18 271:14

278:22 301:5

364:17

12th 4:5

14 338:19

15 7:5 109:2

167:10 203:1

230:15 284:18

379:13 384:14

1942 122:13

1970s 25:21

73:22 210:9

1973 26:2 40:5

119:12

1980s 329:4

1989 14:12

2

2 3:1 284:18

303:19

20 9:16,17 41:16

74:11 141:19

157:3 286:9

2005 181:15

2007 123:11

137:4 158:12

2008 100:5

217:18 271:16

2011 123:13

2013 141:7,10

2014 1:7 4:5,10

16:8 75:17

385:14,19

215 30:17 60:14

90:1,9,14 92:3

103:5 128:16

175:10 181:1

201:5 257:3

278:20 279:9

301:4,11,16

314:20 330:11

351:17 354:3,4

360:14 365:9

369:5

21st 4:10

22nd 1:13

25 229:9

27 141:8

2703 169:3

29th 137:4

3

3 3:9 88:18

110:10 141:8

30 1:14 4:5

109:3 163:19

315 361:1

4

4 3:16 384:14

40 224:8

45 284:18

47 16:9

484 104:8

4g 136:22

5

5 110:10

50 70:12 157:9

157:10 306:12

363:13

51 196:22

520 16:12
Meeting November 12, 2014

202-220-4158 www.hendersonlegalservices.com

Henderson Legal Services, Inc.

70

6

60 39:18 329:14

62 227:19

622 17:11

623 17:12

7

700 220:8

702 31:3 163:11

163:21 164:5

174:3 213:19

256:9 278:22

279:10,19,20

300:11 358:1

74 163:1

75 39:18 163:1

8

8 1:14 4:5

9

9 217:12

99 352:8