Statement of Maciej Cegłowski, Founder, Pinboard
Before the U.S. Senate Committee on Banking, Housing, and Urban Development
On the topic of “Privacy Rights and Data Collection in a Digital Economy”
May 7, 2019
Thank you for the opportunity to address you today.
I am the founder and sole employee of Pinboard, a small for-profit archiving service founded in 2009 thatcompetes in part on the basis of personal privacy. I have also been a frequent critic of Silicon Valley’s
reliance on business models requiring mass surveillance, speaking on the topic at conferences both in the
United States and abroad.
As someone who earns his living through data collection, I am acutely aware of the power the tools weare building give us over our fellow citizens’ private lives, and the danger they pose to our liberty. I am
grateful to Chairman Crapo, ranking member Brown, and the committee for the opportunity to testify on
this vital matter.
The internet economy in 2019 is dominated by five American tech companies: Apple, Microsoft, Google,
Facebook, and Amazon. These are also the five most valuable corporations in the world, with a combined
market capitalization exceeding four trillion dollars1. Between them, these companies control the market
for online advertising, mobile and desktop operating systems, office software, document storage, search,cloud computing, and many other areas of the digital economy. They also own and operate a significant
portion of the physical infrastructure of the internet, and act as its de facto regulating authority.
The concentration of power in the hands of these giant firms is the epilogue to a spectacular story ofAmerican innovation and dynamism. The technologies underpinning the internet were all developed here
in the United States, and the many fortunes that they produced owe their thanks to fruitful cooperation
between government, industry, and the research community. Working together, the public and private
sectors created the conditions for a startup culture unlike any other in the world.
Today, however, that culture of dynamism is at risk. The surveillance business model has eroded user
trust to such a point that it is impeding our ability to innovate.
In many ways, the five internet giants operate like sovereign states. Their operations are global, anddecisions they take unilaterally can affect entire societies. Denmark has gone so far as to send anambassador to Silicon Valley. When Jeff Bezos, the CEO of Amazon, met recently with the Canadianprime minister, the occasion was covered in the press like a state visit.

1

At the time of writing, Amazon was valued at $966B, Microsoft $988B, Apple $974B, Facebook $558B,
and Google (Alphabet) $824B.

The emergence of this tech oligopoly reflects a profound shift in our society, the migration of every areaof commercial, social, and personal life into an online realm where human interactions are mediated bysoftware.
To an extent that has no precedent, the daily activities of most Americans are now tracked andpermanently recorded by automated systems. It is likely that every person in this hearing room carries
with them a mobile phone that keeps a history of their location, is privy to their most private
conversations, and contains a rich history of their private life. Some of you may even have an always-on
microphone in your car or home that responds to your voice commands.
Emerging technologies promise to afford these systems even more intimate glimpses into our private
lives—phones that monitor our facial expressions as we read, and connected homes that watch over us
while we sleep. Scenarios that were once the province of dystopian dime fiction have become an
unremarkable consumer reality.
The sudden ubiquity of this architecture of mass surveillance, and its enshrinement as the default businessmodel of the online economy, mean that we can no longer put off hard conversations about the threats it
poses to liberty.
Adding to this urgency is the empirical fact that, while our online economy depends on the collection andpermanent storage of highly personal data, we do not have the capacity to keep such large collections ofuser data safe over time.
The litany of known data breaches is too long to recite here, but includes every one of the top five techcompanies, as well as health and financial firms and government agencies. Every year brings new andmore spectacular examples of our inability to protect our users. At Yahoo, an internet giant at the timewith a world-class security team, over 3 billion user accounts were compromised in a 2013 breach. In
2015, the US Office of Personnel Management allowed unauthorized access to the records of over four
million people, including many with highly sensitive security clearances. And in 2017, Equifax exposed
data, including social security numbers, on 147 million Americans, nearly half the US population.
While many individual data breaches are due to negligence or poor practices, their overall number reflects
an uncomfortable truth well known to computer professionals—that our ability to attack computer
systems far exceeds our ability to defend them, and will for the foreseeable future.
The current situation, therefore, is not tenable. The internet economy today resembles the earliest daysof the nuclear industry. We have a technology of unprecedented potential, we have made glowing
promises about how it will transform the daily lives of our fellow Americans, but we don’t know how tokeep its dangerous byproducts safe.

Two Views of Privacy

Discussing privacy in the context of regulation can be vexing, because the companies doing the most to
erode our privacy are equally sincere in their conviction that they are its champions.
The confusion stems from two different ways in which we use the word privacy, leading us to sometimes
talk past each other.
In the regulatory context, discussion of privacy invariably means data privacy—the idea of protecting
designated sensitive material from unauthorized access.
Laws like the Health Insurance Portability and Accountability Act (HIPAA) and the Gramm-Leach-Bliley
Act (GLBA) delimit certain categories of sensitive information that require extra protection, and mandateways in which health and financial institutions have to safeguard this data, or report when thosesafeguards have failed. The Children's Online Privacy Protection Act of 1998 extends similar protection
to all data associated with children.
We continue to use this framework of data privacy today, including in the recently enacted General Data
Protection Regulation (GDPR).
It is true that, when it comes to protecting specific collections of data, the companies that profit most fromthe surveillance economy are the ones working hardest to defend them against unauthorized access.
But there is a second, more fundamental sense of the word privacy, one which until recently was so
common and unremarkable that it would have made no sense to try to describe it.
That is the idea that there exists a sphere of life that should remain outside public scrutiny, in which we
can be sure that our words, actions, thoughts and feelings are not being indelibly recorded. This includes
not only intimate spaces like the home, but also the many semi-private places where people gather andengage with one another in the common activities of daily life—the workplace, church, club or union hall.
As these interactions move online, our privacy in this deeper sense withers away.
Until recently, even people living in a police state could count on the fact that the authorities didn’t haveenough equipment or manpower to observe everyone, everywhere2, and so enjoyed more freedom frommonitoring than we do living in a free society today.
A characteristic of this new world of ambient surveillance is that we cannot opt out of it, any more than
we might opt out of automobile culture by refusing to drive. However sincere our commitment towalking, the world around us would still be a world built for cars. We would still have to contend withroads, traffic jams, air pollution, and run the risk of being hit by a bus.
Similarly, while it is possible in principle to throw one’s laptop into the sea and renounce all technology,
it is no longer be possible to opt out of a surveillance society.
2 The record for intensive surveillance in the pre-internet age likely belongs to East Germany, where bysome estimates one in seven people was an informant.
(https://archive.nytimes.com/www.nytimes.com/books/first/k/koehler-stasi.html)

When we talk about privacy in this second, more basic sense, the giant tech companies are not theguardians of privacy, but its gravediggers.
The tension between these interpretations of what privacy entails, and who is trying to defend it,
complicates attempts to discuss regulation.
Tech companies will correctly point out that their customers have willingly traded their private data for analmost miraculous collection of useful services, services that have unquestionably made their lives better,and that the business model that allows them to offer these services for free creates far more value thanharm for their customers.
Consumers will just as rightly point out that they never consented to be the subjects in an uncontrolledsocial experiment, that the companies engaged in reshaping our world have consistently refused tohonestly discuss their business models or data collection practices, and that in a democratic society,profound social change requires consensus and accountability.

Behavioral Data
Further complicating the debate on privacy is the novel nature of the data being collected. While the laws
around protecting data have always focused on intentional communications—documents that can beintercepted, conversations that can be eavesdropped upon—much of what computer systems capture
about us is behavioral data: incidental observations of human behavior that don’t seem to convey any
information at all.
Behavioral data encompasses anything people do while interacting with a computer system. It can include
the queries we type into a search engine, our physical location, the hyperlinks we click on, whether we are
sitting or standing, how quickly we scroll down a document, how jauntily we walk down a corridor,
whether our eyes linger on a photo, whether we start to write a comment and then delete it—even thechanges in our facial expression as we are shown an online ad.
This incidental data has proven to be such a valuable raw material that an entire industry now specializes
in finding ways to mine it. The devices used to spy on us include our computers, cell phones, televisions,cars, security cameras, our children’s toys, home appliances, wifi access points, even at one point trashcans in the street3.
Privacy and Consent

Campbell-Dollaghan, Kelsey. “Brave New Garbage: London’s Trash Cans Track You Using Your
Smartphone”. Gizmodo. (Aug 9, 2013) https://gizmodo.com/brave-new-garbage-londons-trash-cans-trackyou-using-1071610114
3

The extent to which anyone consents—or can consent—to this kind of tracking is the thorny question inattempting to regulate the relationship between people and software.
The General Data Protection Regulation (GDPR), enacted in May of 2018, is the most ambitious attemptthus far to regulate online privacy. It takes a very traditional view of the relationship between people anddata.
In the eyes of the GDPR, people own their data. They make an affirmative choice to share their data with
online services, and can revoke that choice. The consent they give must be explicit and limited to a
specified purpose—the recipient does not have carte blanche to use the data as they please, or to share itwith third parties, with some complicating caveats.
People have the right to request a full download of their data from the services they have entrusted it to,and they have the right to demand that it be permanently erased.
The GDPR imposes a notification requirement for data breaches, and requires affirmative consent for thesale of user data. It also restricts the movement of data to outside jurisdictions (though in the case of theUnited States, this restriction is superseded by the US-EU Privacy Shield framework).
Finally, the GDPR mandates that privacy safeguards like data tokenization and encryption be built in to
new systems, and that companies appoint a dedicated privacy officer.
The GDPR is not a simple regulation, and many of its most potentially significant provisions (such as thescope of a data controller’s ‘legitimate interests’, or what the right to erasure means in the context of amachine learning model) await interpretation by regulators.
What limits, if any, the GDPR will place on the application of machine learning is a particularly
important open question. The law on its face prohibits automated decision making that has a “legal orsimilarly significant effect” on data subjects, but the definition of “significant effect” is not clear, nor is it
clear whether having a human being simply countersign an algorithmic decision would be enough tosatisfy regulators that the decision process is not fully automated.
Impacts
As it is so new, the GDPR’s ultimate impact on online privacy in the EU is unclear. Some of the dramatic
early impacts (like major US newspapers going offline) have proven to be transient, while many of thebiggest impacts hinge on future decisions by EU regulators.
Enough has happened, however, to draw some preliminary conclusions.
The GDPR so far has made life hard on internet users. It is not clear that this is the GDPR’s fault.

The plain language of the GDPR is so plainly at odds with the business model of surveillance advertisingthat contorting the real-time ad brokerages into something resembling compliance has required acrobatics
that have left essentially everybody unhappy.
The leading ad networks in the European Union have chosen to respond to the GDPR by stitching
together a sort of Frankenstein’s monster of consent, a mechanism whereby a user wishing to visit, say, aweather forecast page4 is first prompted to agree to share data with a consortium of 119 entities, including
the aptly named “A Million Ads” network. The user can scroll through this list of intermediaries one byone, or give or withhold consent en bloc, but either way she must wait a further two minutes for theconsent collection process to terminate before she is allowed to find out whether or not it is going to rain.
This majestically baroque consent mechanism also hinders Europeans from using the privacy preserving
features built into their web browsers, or from turning off invasive tracking technologies like third-party
cookies, since the mechanism depends on their being present.
For the average EU citizen, therefore, the immediate effect of the GDPR has been to add friction to theirinternet browsing experience along the lines of the infamous 2011 EU Privacy Directive (“EU cookielaw”) that added consent dialogs to nearly every site on the internet.
The GDPR rollout has also demonstrated to what extent the European ad market depends on Google, who
has assumed the role of de facto technical regulatory authority due to its overwhelming market share5.
Google waited until the night before the regulation went into effect to announce its intentions, leaving ad
networks scrambling.
It is significant that Google and Facebook also took advantage of the US-EU privacy shield to move 1.5
billion non-EU user records out of EU jurisdiction to servers in the United States. Overall, the GDPR has
significantly strengthened Facebook and Google at the expense of smaller players in the surveillance
economy.
The data protection provisions of the GDPR, particularly the right to erase, imposed significant
compliance costs on internet companies. In some cases, these compliance costs just show the legislation
working as intended. Companies who were not keeping adequate track of personal data were forced toretrofit costly controls, and that data is now safer for it.
But in other cases, companies with a strong commitment to privacy also found themselves expending
significant resources on retooling. Personally identifying information has a way of seeping in to odd
corners of computer systems (for example, users will sometimes accidentally paste their password into asearch box), and tracking down all of these special cases can be challenging in a complex system. The
requirements around erasure, particularly as they interact with backups, also impose a special burden, as
4This is an actual example.
Google has at least a 70% advertising market share in Europe, though this figure is averaged over theten year period 2006-2016 and likely far higher today. Laurent, Lionel. "Europe Is Changing Google for
the Better”. Washington Post (March 20, 2019). https://www.washingtonpost.com/business/europe-ischanging-google-for-the-better/2019/03/20/691aaff4-4b2e-11e9-8cfc-2c5d0999c21e_story.html
5most computer systems are designed with a bias to never losing data, rather than making it easy to
expunge.
A final, and extremely interesting outcome of the GDPR, was an inadvertent experiment conducted by the
New York Times. Privacy advocates have long argued that intrusive third-party advertising does notprovide more value to publishers than the traditional pre-internet style of advertising based off of content,
but there has never been a major publisher willing to publicly run the experiment.
The New York Times tested this theory by cutting off all ad networks in Europe, and running only direct
sold ads to its European visitors. The paper found that ad revenue increased significantly, and stayed
elevated into 2019, bolstering the argument that surveillance-based advertising offers no advantage to
publishers, and may in fact harm them.6

The Limits of Consent
While it is too soon to draw definitive conclusions about the GDPR, there is a tension between its concept
of user consent and the reality of a surveillance economy that is worth examining in more detail.
A key assumption of the consent model is any user can choose to withhold consent from online services.
But not all services are created equal—there are some that you really can’t say no to.
Take the example of Facebook. Both landlords and employers in the United States have begun demanding
to see Facebook accounts as a condition of housing or employment7. The United States Border Patrol has
made a formal request to begin collecting social media to help vet people arriving in the country8. In boththose contexts, not having a Facebook account might stand out too much to be a viable option. Many
schools now communicate with parents via Facebook; Facebook groups are also the locus for politicalorganizing and online activism across the political spectrum.
Analogous arguments can be made for social products offered by the other major tech companies. But if
you can’t afford to opt out, what does it mean to consent?

Davies, Jessica. “After GDPR, the New York Times cut off ad exchanges in Europe—and kept growing
ad revenue”. Digiday (Jan 6, 2019)., https://digiday.com/media/gumgumtest-new-york-times-gdpr-cut-offad-exchanges-europe-ad-revenue/
7
Dewey, Caitlin. "Creepy startup will help landlords, employers and online dates strip-mine intimate datafrom your Facebook page.” Washington Post, (Jun 9, 2016). https://www.washingtonpost.com/news/theintersect/wp/2016/06/09/creepy-startup-will-help-landlords-employers-and-online-dates-strip-mineintimate-data-from-your-facebook-page/8
81 FR 40892
https://www.federalregister.gov/documents/2016/06/23/2016-14848/agency-information-collectionactivities-arrival-and-departure-record-forms-i-94-and-i-94w-and#h-11
6

Opting out can also be impossible because of how deeply the internet giants have embedded themselvesin the fabric of the internet. For example, major media properties in the EU use a technology called
ReCaptcha on their GDPR consent forms9. These forms must be completed before a user can access thewebsite they are gathering consent for, but since the ReCaptcha service is run by Google, and the formcannot be submitted without completing the Google-generated challenge (which incidentally performs
free image classification labor for the company), a user who refuses to give Google access to her browser
will find herself denied access to a large portion of the internet.
While this specific example may change when it comes to the attention of an EU regulator, the broader
issue remains. The sheer reach of the tech oligopoly makes it impossible to avoid using their services.
When a company like Google controls the market-leading browser, mobile operating system, email
service and analytics suite, exercises a monopoly over search in the EU, runs the largest ad network in
Europe, and happens to own many of the undersea cables that connect Europe to the rest of the world10,
how do you possibly say ‘no’?
Informed Consent
Beyond one’s basic ability to consent, there is the question of what it means to give informed consent.
Presumably we are not opting in or out of the services we use for capricious reasons, but because we canmake a rational choice about what is in our interest.
In practice, however, obtaining this information is not possible, even assuming superhuman reserves ofpatience.
For example, anyone visiting the popular Tumblr blogging platform from a European IP address mustfirst decide whether to share data with Tumblr’s 201 advertising partners, and read five separate privacy
policies from Tumblr’s several web analytics providers.
Despite being a domain expert in the field, and spending an hour clicking into these policies, I am unable
to communicate what it is that Tumblr is tracking, or what data of mine will be used for what purposes by
their data partners (each of whom has its own voluminous terms of service). This opacity exists in part
because the intermediaries have fought hard to keep their business practices and data sharing processes a
secret, even in the teeth of strong European regulation.
Organizations like the Interactive Advertising Bureau Europe (IABE) defeat the spirit of the GDPR bybundling consent and requiring it across many ad-supported properties in Europe. If regulators block thebundling in its current incarnation, it will no doubt rise from the dead in a modified form, reflecting theundying spirit of surveillance advertising. But at no point will internet users have the information they9

The purpose of ReCaptcha is to prevent automated submissions, and ensure that a human being isfilling out the form.
10
Zimmer, Jameson. ”Google Owns 63,605 Miles and 8.5% of Submarine Cables Worldwide.”
Broadband Now, (September 12, 2018) https://broadbandnow.com/report/google-content-providerssubmarine-cable-ownership/

would need to make a truly informed choice (leaving aside the ridiculousness of requiring a legal
education and two hours of sustained close reading in order to watch a cat video).
Consent in a world of inference
Finally, there is a sense in which machine learning and the power of predictive inference may be making
the whole idea of consent irrelevant. At this point, companies have collected so much data about entire
populations that they can simply make guesses about us, often with astonishing accuracy11.
A useful analogy here is a jigsaw puzzle. If you give me a puzzle with one piece missing, I can still
assemble it, reconstruct the contours of the missing piece by looking at the shape of the pieces around it
and, if the piece is small compared to the whole, easily interpolate the missing part of the image.
This is exactly what computer systems do to us when we deny them our personal information. Experts
have long known that it takes a very small amount of data to make reliable inferences about a person.
Most people in the United States, for example, can be uniquely identified by just the combination of theirdate of birth, gender, and zip code12.
But machine learning is honing this ability to fill in the blanks to surprising levels of accuracy, raisingtroubling questions about what it means to have any categories of protected data at all.
For example, imagine that an algorithm could inspect your online purchasing history and, with highconfidence, infer that you suffer from an anxiety disorder. Ordinarily, this kind of sensitive medical
information would be protected by HIPAA, but is the inference similarly protected? What if the algorithm
is only reasonably certain? What if the algorithm knows that you’re healthy now, but will suffer fromsuch a disorder in the future?
The question is not hypothetical—a 2017 study13 showed that a machine learning algorithm examining
photos posted to the image-sharing site Instagram was able to detect signs of depression before it wasdiagnosed in the subjects, and outperformed medical doctors on the task.
The paradigm of automatic ownership of personal data does not mesh well with a world where suchprivate data can not only interpolated and reconstructed, but independently discovered by an algorithm!
And if I can infer such important facts about your life by applying machine learning to public data, then I
have deprived you of privacy just as effectively as I would have by direct eavesdropping.
11

The line of argument in this section is adapted from the work of Dr. Zeynep Tufekci, UNC Chapel Hill.
For example, “Think You’re Discreet Online? Think Again” (April 21, 2019)https://www.nytimes.com/2019/04/21/opinion/computational-inference.html
12
Sweeney, Latanya. (2000) “Simple Demographics Often Identify People Uniquely”, Carnegie Mellon
University, Data Privacy Working Paper. https://dataprivacylab.org/projects/identifiability/paper1.pdf
13
Reece, Andrew and Danforth, Cristopher. (2017) "Instagram photos reveal predictive markers ofdepression” EPJ Data Science https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688017-0110-z

In order to talk meaningfully about consent in online systems, the locus of regulation will need to expand
beyond data collection, to cover how those data collections, and the algorithms trained on them, are used.
But to do this, we will first need far greater visibility into the workings of surveillance-dependent techcompanies than they have so far been willing to grant us.
As it stands, the consent framework exemplified in the GDPR is simply not adequate to safeguard
privacy. As much as we would like to be the masters of our data, we are not. And the real masters aren’t
talking.
Goals for Privacy Regulation
Absent a clear understanding of how our data is being used, and the role it plays in surveillance-based
business models, it is hard to lay out a specific regulatory program.
Nevertheless, there are some general goals we can pursue based on the experience of regulation attempts
in Europe, and what we know about the surveillance economy.
Clarity
Privacy regulation should be understandable, both for users of the technology, and for the companies theregulations govern. Users especially should not be required to make complex and irrevocable decisions
about privacy. To the extent possible, intuitions about privacy from the human world (“a casualconversation between friends is not recorded forever”) should carry over into the digital world.
Privacy
At the risk of sounding tautological, privacy regulation should not punish people for seeking privacy. Itshould not be necessary to turn on invasive tracking technologies in one’s browser in order to express thedesire to not to be tracked.
Retention Limits on Behavioral Data
Knowing that we lack the capacity to keep data collections safe over time, we can reduce the potential
impact of any breach by setting strict lifetimes for behavioral data.
Google has demonstrated the feasibility of this approach with their recent announcement that users will beable to set their account to automatically delete location data after three or 18 months14. This

14

Monsees, David and McGriff, Marlo. "Introducing auto-delete controls for your Location History andactivity data.” (2019, May 1). https://www.blog.google/technology/safety-security/automatically-deletedata/demonstrates that permanent retention of behavioral data is not critical to surveillance-based businessmodels. Such limits should be enforced industrywide.
Moving to a norm where behavioral data is kept briefly instead of forever will mark a major step forward
in data security, both reducing the time data is potentially exposed to attackers, and reducing the total
volume of data that must be kept safe.
Time limits on behavioral data will also reduce consumers’ perception that they are making irrevocableprivacy commitments every time they try a new product or service.
Right To Download
The right to download is one of the most laudable features in the GDPR, and serves the importantsecondary purpose of educating the public about the extent of data collection.
This right should, however, be expanded to include the right to download, and correct, all informationthat third-party data brokers have provided about a user, in a spirit similar to the Fair Credit ReportingAct.
Fairness
Tech startups in the highly regulated areas of health, finance and banking should be required to compete
on the same regulatory footing as established businesses in those areas. In particular, they should not beallowed to do an end run around existing data privacy laws by using machine learning and algorithmic
inference.
For example, the use of a machine learning algorithm should not allow a loan company to evadeconsumer protections against discrimination in fair lending laws.
(For a fuller discussion of this point, see the addendum on machine learning at the end of this document).
Positive Regulation
While the above suggestions seek to impose limits and restrictions, there is an important way that privacy
regulation can create new ground for innovation.
What is missing from the regulatory landscape is a legal mechanism for making credible and bindingpromises to users about privacy practices.
Today, internet startups in the U.S. who want to compete on privacy have no mechanism to signal theircommitment to users other than making promises through their terms of service (which usually include astandard legal clause that they may change at any time).

Except in the case of the most egregious violations, which sometimes attract the attention of the Federal
Trade Commission, these terms of service carry little weight.
As the owner of a company that markets itself to privacy-conscious people, I would derive enormous
benefit from a legal framework that allowed me to make binding privacy promises (for example, a pledge
that there is no third-party tracking on my website), and imposed stiff fines on my company if I violated
these guarantees (including criminal liability in the case of outright fraud).
Such a legal mechanism would not only enable competition around privacy-enhancing features, but itwould also give future regulators a clearer idea of how much value consumers place on data privacy. It is
possible that the tech giants are right, and people want services for free, no matter the privacy cost. It isalso possible that people value privacy, and will pay extra for it, just like many people now pay a
premium for organic fruit. The experiment is easy to run—but it requires a modest foundation in law.
Academic research in computer science is full of fascinating ideas that could serve as the seed forbusiness built around user privacy. Results in fields like homeomorphic encryption, differential privacy,
privacy-preserving machine learning, and zero-knowledge proofs all await a clever entrepreneur who can
incorporate them into a useful product or service. It is very hard to compete against companies likeAmazon or Facebook on price, but it is not hard to beat them on privacy. With a minimum of regulatory
scaffolding, we might see a welcome new burst of innovation.
Preserving Liberty
The final, and paramount goal, of privacy regulation should be to preserve our liberty.
There is no clearer warning of the danger of building up an infrastructure of surveillance than what ishappening today in China’s Xinjiang Uygur Autonomous Region. Claiming to be concerned about thepossible radicalization of a Muslim minority, Chinese authorities have imposed a regime of total
surveillance over a population of twenty-five million people.
As recent reporting by Human Rights Watch has shown, a computer system called the Integrated Joint
Operations Platform (IJOP) monitors the location and movement of all people in the province (based onphone data), as well as their gas and electricity consumption, which apps they use, where they worship,
who they communicate with, and how they spend their money. This surveillance information is fed intomachine learning models that can bin people into one of thirty-six suspect categories, bringing them to thecloser attention of the police15. Never before has a government had the technical means to implement thislevel of surveillance across an entire population. And they are doing it with the same off-the-shelfcommercial technologies we use in America to get people to click on ads.

Human Rights Watch, “China’s Algorithms of Repression”, (May 1 2019)https://www.hrw.org/report/2019/05/01/chinas-algorithms-repression/reverse-engineering-xinjiang-policemass-surveillance
15

The latent potential of the surveillance economy as a toolkit for despotism cannot be exaggerated. The
monitoring tools we see in repressive regimes are not ‘dual use’ technologies—they are single use
technologies, working as designed, except for a different master.
For sixty years, we have called the threat of totalitarian surveillance ‘Orwellian’, but the word no longer
fits the threat. The better word now may be ‘Californian’. A truly sophisticated system of social control,of the kind being pioneered in China, will not compel obedience, but nudge people towards it. Rather thancensoring or punishing those who dissent, it will simply make sure their voices are not heard. It will
reward complacent behavior, and sideline troublemakers. It’s even possible that, judiciously wielded,
such a system of social control might enjoy wide public support in our own country.
But I hope you will agree with me that such a future would be profoundly un-American.
There is no deep reason that weds the commercial internet to a business model of blanket surveillance.
The spirit of innovation is not dead in Silicon Valley, and there are other ways we can grow our digital
economy that will maintain our lead in information technology, while also safeguarding our liberty. Just
like the creation of the internet itself, the effort to put it on a safer foundation will require a combinationof research, entrepreneurial drive and timely, enlightened regulation. But we did it before, and there’s noreason to think we can’t do it again.

Addendum: Machine Learning and Privacy
Machine learning is a mathematical technique for training computer systems to make accurate predictions
from a large corpus of training data, with a degree of accuracy that in some domains can mimic humancognition.
For example, machine learning algorithms trained on a sufficiently large data set can learn to identify
objects in photographs with a high degree of accuracy, transcribe spoken language to text, translate texts
between languages, or flag anomalous behavior on a surveillance videotape.
The mathematical techniques underpinning machine learning, like convolutional neural networks (CNN),
have been well-known since before the revolution in machine learning that took place beginning in 2012.
What enabled the key breakthrough in machine learning was the arrival of truly large collections of data,
along with concomitant computing power, allowing these techniques to finally demonstrate their fullpotential.
It takes data sets of millions or billions of items, along with considerable computing power, to getadequate results from a machine learning algorithms Before the advent of the surveillance economy, we
simply did not realize the power of these techniques when applied at scale.
Because machine learning has a voracious appetite for data and computing power, it contributes both tothe centralizing tendency that has consolidated the tech industry, and to the pressure companies face tomaximize the collection of user data.
Machine learning models poses some unique problems in privacy regulation because of the way they canobscure the links between the data used to train them and their ultimate behavior.
A key feature of machine learning is that it occurs in separable phases. An initial training phase consistsof running a learning algorithm on a large collection of labeled data (a time and computation-intensive
process). This model can then be deployed in an exploitation phase, which requires far fewer resources.
Once the training phase is complete, the data used to train the model is no longer required and canconceivably be thrown away.
The two phases of training and exploitation can occur far away from each other both in space and time.
The legal status of models trained on personal data under privacy laws like the GDPR, or whether datatransfer laws apply to moving a trained model across jurisdictions, is not clear.
Inspecting a trained model reveals nothing about the data that went into it. To a human inspecting it, themodel consists of millions and millions of numeric weights that have no obvious meaning, or relationshipto human categories of thought. One cannot examine an image recognition model, for example, and point
to the numbers that encode ‘apple’.

The training process behaves as a kind of one-way function. It is not possible to run a trained model
backwards to reconstruct the input data; nor is it possible to “untrain” a model so that it will forget a
specific part of its input.
Machine learning algorithms are best understood as inference engines. They find structure and excel atmaking inferences from data that can sometimes be surprising even to people familiar with thetechnology. This ability to see patterns that humans don’t notice has led to interest in using machinelearning algorithms in medical diagnosis, evaluating insurance risk, assigning credit scores, stock trading,and other fields that currently rely on expert human analysis.
The opacity of machine learning models, combined with this capacity for inference, also make them an
ideal technology for circumventing legal protections on data use. In this spirit, I have previously referred
to machine learning as “money laundering for bias”. Whatever latent biases are in the training data,
whether or not they are apparent to humans, and whether or not attempts are made to remove them fromthe data set, will be reflected in the behavior of the model.
A final feature of machine learning is that it is curiously vulnerable to adversarial inputs. For example, animage classifier that correctly identifies a picture of a horse might reclassify the same image as an apple,
sailboat or any other object of an attacker’s choosing if they can manipulate even one pixel in the image16.
Changes in input data not noticeable to a human observer will be sufficient to persuade the model.
Recent research suggests that this property is an inherent and ineradicable feature of any machine learning
system that uses current approaches17.
In brief, machine learning is effective, has an enormous appetite for data, requires large computational
resources, makes decisions that resist analysis, excels at finding latent structure in data, obscures the link
between source data and outcomes, defies many human intuitions, and is readily fooled by aknowledgeable adversary.

Su, Jiawei, Vargas, Danilo, and Kouichi, Sakurai. “One Pixel Attack for Fooling Deep Neural Networks”(2017, Oct 24). https://arxiv.org/pdf/1710.08864.pdf
16

17

Wang, Xianmin, Li, Jing, Kuang, Xioahui, Tan, Yu-an. Journal of Parallel and Distributed
Computing(August 2019) “The security of machine learning in an adversarial setting: A survey”