Office of the Director of National Intelligence 

2012 Data Mining Report 

For the Period January 1, 2012 through December 31, 2012

 
2 

Office of the Director of National Intelligence 

2012 Data Mining Report 

January 1, 2012 through December 31, 2012 

I. Introduction 

The Office of the Director of National Intelligence (ODNI) provides this report pursuant to 

Section 804 of the Implementing the Recommendations of the 9/11 Commission Act of 2007, 

entitled The Federal Agency Data Mining Reporting Act of 2007 (Act). 

A. Scope

This report covers the activities of all ODNI components from January 1, 2012 through 

December 31, 2012. Constituent elements of the Intelligence Community (IC) will 

report their activities to Congress through their own departments or agencies.1

 

B. Reporting Requirement

The Data Mining Reporting Act requires “the head of each department or agency of the 

Federal Government that is engaged in an activity to use or develop data mining shall 

submit a report to Congress on all such activities of the department or agency.”2

 This is 

an annual requirement. Under the Act, “data mining” is defined as: 

“... a program involving pattern-based queries, searches or other analyses of one or 

more electronic databases, where — 

(A) a department or agency of the Federal Government, or a non-Federal entity 

acting on behalf of the Federal Government, is conducting the queries, 

searches, or other analyses to discover or locate a predictive pattern or 

anomaly indicative of terrorist or criminal activity on the part of any 

individual or individuals; 

(B) the queries, searches, or other analyses are not subject-based and do not use 

personal identifiers of a specific individual, or inputs associated with a 

specific individual or group of individuals, to retrieve information from the 

database or databases; and 

(C) the purpose of the queries, searches, or other analyses is not solely— (i) the 

detection of fraud, waste, or abuse in a Government agency or program; or (ii) 

the security of a Government computer system. ”3

When focusing on individuals or groups, the ODNI typically uses analytic tools and 

techniques that rely on “personal identifiers of a specific individual, or inputs 

associated with a specific individual or group of individuals,” such as link-analysis 

 

1

 Section 804(c)(1) of the Data Mining Reporting Act 

2

 Section 804(c)(1) of the Data Mining Reporting Act

3

 Section 804(b)(1)(A) of the Data Mining Reporting Act 
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tools. Unlike the predictive, pattern-based technologies envisioned by the Act, these 

tools and techniques utilized by ODNI components start with a known or suspected 

terrorist, or other subject of foreign intelligence interest, and use various methods to 

uncover links or relationships between the known subject and potential associates or 

other persons with whom that subject has a “link” (a contact or relationship). Thus, 

such analytic tools and techniques do not fall within the statutory definition of data 

mining. 

C. Report Content:

 

Part II of this report describes those ODNI programs that meet the reporting 

requirements of the Data Mining Act. For this year’s submission, there are no 

reportable programs. 

Part III of this report includes discussion of three programs from the Intelligence 

Advanced Research Projects Activity (IARPA), all of which were included in last 

year’s report. Although these programs are not “data mining” programs, information is 

nonetheless provided in the interest of transparency. 

Two of the programs, the Knowledge Discovery and Dissemination (KDD) Program 

and the Automated Low-Level Analysis and Description of Diverse Intelligence Video 

(ALADDIN Video) Program, were again included in this year’s report as technologies 

explored by, or later developed from, these programs could be used to support future 

data mining activities. The Security and Privacy Assurance Research (SPAR) Program, 

the third program, was included to provide information on research that may have 

applications in enhancing security and privacy protections in data mining activities. 

Descriptions of these programs are provided in narrative form, with information 

generally responsive to the reporting elements of the Data Mining Reporting Act. 

D. Protection of Privacy and Civil Liberties 

The ODNI Civil Liberties and Privacy Office (CLPO) works closely with the ODNI 

Office of General Counsel, ODNI components and the IC elements to ensure 

appropriate legal, privacy, and civil liberties safeguards are incorporated into policies, 

processes and procedures that support the intelligence mission. The CLPO is led by the 

Civil Liberties Protection Officer, a position established by the Intelligence Reform and 

Terrorism Prevention Act of 2004 (IRTPA). The duties of this Officer are set forth in 

that Act, and include: ensuring that the protection of civil liberties and privacy is 

appropriately incorporated in the policies of the ODNI and the IC; overseeing 

compliance by the ODNI with legal requirements relating to civil liberties and privacy; 

reviewing complaints about potential abuses of privacy and civil liberties in ODNI 

programs and activities; and ensuring that technologies sustain, and do not erode, 
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privacy protections relating to the use, collection, and other disclosure of personal 

information.”4

The IC has in place a protective infrastructure built in principal part on a core set of 

U.S. Person rules derived from Executive Order (EO) 12333. This EO requires each IC 

element to maintain procedures, approved by the Attorney General, governing the 

collection, retention and dissemination of U.S. Person information. These procedures 

limit the type of information that may be collected, retained or disseminated to the 

categories listed in part 2.3 of the EO. Each IC element’s Attorney General-approved 

U.S. Persons guidance is interpreted, applied, and overseen by that element’s Office of 

General Counsel, Office of Inspector General, and other compliance offices as 

appropriate. Violations are reported to the Intelligence Oversight Board of the 

President’s Intelligence Advisory Board. In addition to EO 12333, IC elements are 

subject to the requirements of the Privacy Act, which protects information about U.S. 

citizens and permanent resident aliens that a government agency maintains and 

retrieves by name or unique identifier. 

The IC’s privacy and civil liberties protective infrastructure is bolstered also by 

guidance and directives issued by the Office of Management and Budget, including 

memoranda regarding the reporting of and response to incidents involving personally 

identifiable information and the minimization of Social Security Numbers. 

Before any tool or technology could be used in an operational setting, the use of the 

tool or technology would need to be examined pursuant to EO 12333, the Privacy Act, 

and other applicable requirements to determine how the tool could be used consistent 

with the framework for protecting information about Americans and other U.S. 

Persons. 

CLPO has been considering how advanced technologies, employed in accordance with 

proper laws and policies, enable sharing and use of information while protecting 

privacy and civil liberties. Such privacy-enhancing technologies (PETs) also prove 

useful in providing protections for data mining activities. CLPO is making available the 

results of its PET-related research to IC elements and other government offices with 

which CLPO collaborates, and continues to consider how to make use of PETs for 

intelligence activities. 

II. ODNI Data Mining Activities 

The ODNI did not engage in any activities to use or develop data mining functionality during the 

reporting period. 

4

 National Security Act of 1947, as amended by the Intelligence Reform and Terrorism Prevention Act of 2004 

(IRTPA), 50 U.S.C. §403-3d. 
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III. Other Programs 

The following programs are reported in the interest of transparency. 

A. IARPA Research Programs

The mission of IARPA is to invest in high-risk/high payoff research programs that have 

the potential to provide the U.S. with an overwhelming intelligence advantage over its 

future adversaries. It does not have an operational mission and it does not deploy 

technologies directly to the field. As a scientific research funding organization, IARPA 

does not use, nor does it expect to make use of, data mining technology. 

IARPA programs are by nature experimental and are designed to produce new 

capabilities. The end goal of an IARPA program is typically a proof-of-concept 

experiment or prototype of an entirely new capability. Due to their high-risk research 

nature, IARPA programs do not always achieve their end goals, and when they do, 

further steps are required to transform the results into real world applications. Any 

results from IARPA research programs that do get incorporated into future operational 

programs within the IC, or other parts of the U.S. government, will be subject to 

appropriate legal, privacy, civil liberties and policy safeguards. 

In the interest of transparency, we are including summaries of three IARPA programs 

due to the potential that technologies explored by, or later developed from, those 

programs might be ultimately used to support data mining (such technologies would 

then be subject to reporting under the Data Mining Reporting Act). 

 

1. Knowledge Discovery and Dissemination (KDD) Program. The KDD 

scientific research program began in 2009. A Broad Agency Announcement (BAA) for 

KDD was released on December 22, 2009 and KDD research contracts were awarded 

in September 2010. The KDD program completed its second period in November 2012. 

The objective of the KDD program is to enable an analyst to utilize large, complex and 

varied data sets that he has not seen before to produce actionable intelligence in a 

timely manner. KDD tackles two significant technical areas: (1) how to quickly 

understand the novel data sets so that the contents can be correctly integrated with data 

sets that are already in use (this is termed “alignment”); (2) how to construct automatic 

analysis tools that are able to work effectively across multiple aligned data sets. KDD 

research results will be evaluated using realistic challenge problems throughout the 

program. 

In evaluations of the research team’s prototypes, the KDD scientific research program 

utilizes real-world, classified data sets that are large and complex. KDD researchers’ 

work is evaluated in the context of challenge problems using these data sets. The 

challenge problems are not problems that require data mining technology as defined by 

the Act. The data sets used by researchers are highly varied, and the use of all data sets 

is consistent with all U.S. laws and regulations. 
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2. Automated Low-level Analysis and Description of Diverse Intelligence Video 

(ALADDIN Video) Program. The ALADDIN Video scientific research program 

released a BAA in June 2010, and research contracts were awarded in February 2011. 

The ALADDIN program completed its second round of testing in the fall of 2012. 

The objective of the ALADDIN program is to enable an analyst to query large video 

data sets to quickly and reliably locate those video clips that show a specific type of 

event. The ALADDIN program is researching technologies designed to automatically 

search large numbers of video data files for analyst-defined events of interest and direct 

the analyst to those video data files that are likely to contain occurrences of those 

events. ALADDIN’s technologies, if successful, will help to automate a triage process 

that is currently performed largely manually by analysts. Although this is not “data 

mining,” technologies that result from ALADDIN research could, potentially, be 

applied by operational organizations to support capabilities that involve pattern 

recognition. 

ALADDIN research addresses three significant technical areas: (1) high-speed 

processing of large amounts of video clips to extract information that can later be used 

to support queries about each clip’s contents; (2) generation of effective queries from 

small sets of example video clips and a textual description; (3) robust query processing 

that identifies the clips of interest and summarizes the rationale for their selection. 

ALADDIN research results will be evaluated by IARPA and the National Institute for 

Standards and Technology (NIST). 

The ALADDIN program uses video data files in its research and evaluations that are 

acquired by NIST for its annual, international video search technology research 

program (TRECVID). TRECVID sponsors public evaluations of video and multimedia 

search technologies that are open to worldwide participation. ALADDIN performers 

will participate in these evaluations to demonstrate objective progress in their research. 

The data collections used in the TRECVID evaluations are made available to all 

participants through an evaluation participation agreement that stipulates that the 

TRECVID data collections are to be used for research purposes only. The TRECVID 

data is collected using a rigorous process that protects privacy. 

3. Security and Privacy Assurance Research (SPAR) Program. The SPAR 

program is a follow-on effort to the Automatic Privacy Protection (APP) program 

discussed in the 2009 and 2010 ODNI Data Mining Reports. Neither the SPAR nor 

APP programs involve data mining, but the research results from both programs may 

enhance security and protect privacy in data mining activities. 

The APP program ended in 2010 after achieving two goals. First, it developed secure 

distributed private information retrieval (PIR) protocols that permit an entity (Client) to 

query a cooperating data provider (Server) and retrieve only the records that match the 

query without the Server learning what query was posed or what results were returned. 

These protocols are able to add only minimal overheads in computation and 
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communication for simple queries and databases by using a cooperating third party who 

has access only to encrypted data. Second, APP demonstrated algorithms to determine 

automatically if complex queries are in compliance with privacy policies. This allows a 

Client's auditor with access to the policy and the query history to rapidly verify that 

only authorized queries have been submitted to the Server. 

The SPAR program was launched in 2011 to build on the successes of APP and explore 

additional applications of PIR to realistic IC scenarios. The program will complete its 

first phase of research in March 2013. SPAR includes research projects in three 

technical areas. The first technical area protects security and privacy for database 

access. Unlike the simple queries and static databases of APP, SPAR will investigate 

protocols that handle multiple types of complex queries and databases whose records 

are frequently created, deleted, or updated. In addition, the protocols must integrate 

policy compliance checking with the security and privacy assurances so that the Server 

can verify that a query is compliant with a policy even though the query is never 

learned. The second technical area will build on advances in fully homomorphic 

encryption (FHE) schemes to implement PIR without relying on any third parties. FHE 

is a recent breakthrough result of thirty years of cryptographic research, but current 

schemes are impractical due to high costs in time and memory. SPAR will attempt to 

explore gains in performance by modified FHE schemes that support only the 

computations necessary for information retrieval. The third technical area will 

investigate applications of PIR to the specialized information sharing architectures of 

publish/subscribe systems. 

If successful, the SPAR program will benefit the IC by securing and protecting the 

privacy interests of both the custodians and the consumers of data. The technology 

may enhance cooperative information sharing within the IC, and among government 

and the private sector, by expanding policy options for satisfying security and privacy 

concerns when information is shared.