Big Data, intelligence, and analyst privacy: investigating information dissemination at an NSA-funded research lab

Abstract

The Laboratory for Analytic Sciences (LAS) at North Carolina State University, funded by the National Security Agency, is a collaborative, long-term research enterprise focused on improving intelligence analysis using Big Data. In its work, LAS has recently begun dealing with the trade-off between the collection, storage, and use of large unclassified data-sets and analyst privacy. We discuss particular privacy challenges at LAS, analyze privacy principles in the life cycle of LAS unclassified data-sets, what intelligence analysts themselves think about these privacy concerns, and recommend possible best practices potentially applicable to LAS, as well as future Big Data laboratories and research centers that collaborate with intelligence communities.

Acknowledgments

This material is based upon work supported in whole or in part with funding from the Laboratory for Analytic Sciences (LAS). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the LAS and/or any agency or entity of the United States Government.

Notes

1. EPIC (Electronic Privacy Information Center), “Big Data”. The MacKensey Global Institute expands this definition to note that:

“Big Data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. This definition is intentionally subjective and incorporates a moving definition of how big a data-set needs to be in order to be considered Big Data – i.e., we don’t define Big Data in terms of being larger than a certain number of terabytes (thousands of gigabytes). We assume that, as technology advances over time, the size of datasets that qualify as Big Data will also increase. Also note that the definition can vary by sector, depending on what kinds of software tools are commonly available and what sizes of datasets are common in a particular industry. With those caveats, Big Data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes).

See Manyika et al., “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” 1.

2. This estimate was provided via email by Trey Overman, Director of Programs at LAS May 21, 2017.

3. These statistics were reported by Alyson Wilson, the Principal Investigator for NC State, at the LAS 2016 Winter Symposium, Raleigh, North Carolina December 5, 2017.

4. Metcalf, Keller, and Boyd, “Perspectives on Big Data.”

5. We have been involved directly as members of the privacy team, and [X] is supported through LAS funding for this research.

6. For some examples of this type of research, see Thakur et al., “Enabling Self-Awareness for Knowledge Workers”; Jones et al., “A Versatile Platform.”

7. Executive Office of the President, “Report to the President,” ix.

8. Executive Office of the President, “Big Data,” 11.

9. Ibid., 22.

10. Ibid.

11. The White House, “Computer Data Privacy.”

12. Among other things, the ECPA regulated devices that collected and stored information about communications, such as ‘pen registers’ and ‘trap and trace’ devices. The standards required to obtain warrants for these devices arguably became relatively low as technology expanded, and allowances for these devices were expanded with the 2001 USA PATRIOT Act.

13. Obama, “Remarks by the President.”

14. Hundt, “Making No Secrets,” 581–598; Mueller, “Privacy Concerns in Data Mining,” 529–544.

15. EPIC, “Big Data.”

16. Crawford and Schultz, “Big Data and Due Process.”

17. Zhu et al., “Privacy-Preserving Data Publication,” 549–571; Perera et al., “Big Data Privacy,” 32–39.

18. Tao et al., “Anti-Corruption Privacy-Preserving Publication,” 725–734.

19. Mueller, “Privacy Concerns in Data Mining,” 533.

20. Ibid.

21. Ibid.

22. Perera et al., “Big Data Privacy,” 1.

23. Stumpf et al., “Predicting User Tasks”; Levy, “The Contexts of Control,” 160–174.

24. Defense Security Service, “2015 DSS Trend Report.”

25. Ibid.

26. Federal Bureau of Investigation, “Higher Education and National Security,” 1.

27. Interview with anonymous US federal counterintelligence agency, January 19, 2016, Raleigh, NC.

28. Ibid.

29. Moreau and Saklikar, “Accelerating the Analyst Workflow.”

30. Interview with anonymous staff member, October 2015, Raleigh, NC.

31. Navarro-Arribas and Torra, “Information Fusion in Data Privacy,” 235–244.

32. Google, “Google Security Whitepaper.” Interview with David Hoffman, March 1, 2016, Raleigh, NC; Interview with Judith Beach, March 15, 2016, Raleigh, NC.

33. Information and Privacy Commissioner of Ontario, “Introduction to Privacy.”

34. OPM (Office of Personnel Management), “Privacy Policy.”

35. RTI International, “Privacy Policy.”

36. Interview with Brian Klemm, March 2, 2016, Raleigh, NC.

37. Swire and Ahmad, Foundations of Information Privacy.

38. Executive Office of the President, “Report to the President.”

39. Intel, “Intel Privacy Notice.”

40. Google, “Google Security Whitepaper.”

41. Interview with David Hoffman, March 1, 2016, Raleigh, NC; Interview with Judith Beach, March 15, 2016, Raleigh, NC; Interview with Brian Klemm, March 2, 2016, Raleigh, NC.

42. Interview with David Hoffman, March 1, 2016, Raleigh, NC.

43. Sengers et al., “Reflective Design,” 49–58; Sengers et al., “Culturally Embedded Computing,” 14–21.