Privacy-Enhanced Linking

Abstract

While computer scientists are uniquely situated to incorporate privacy protections in the link analysis algorithms they construct, most computer scientists are unaware of this opportunity and of ways to think about achieving needed protections. The work presented in this writing introduces a new way for computer scientists to think about providing privacy protection within link analysis and introduces the notion of “privacy-enhanced linking” as algorithms that perform link analysis with guarantees of privacy protection modeled after the Fair Information Practices. In this approach, privacy protection is realized by assessing the validity and interpretation of link analysis results such that inappropriate harm to individuals is provably minimized.

Keywords: homeland security, privacy-preserving surveillance, privacy-enhanced technology (PET), privacy, link analysis, Fair Information Practices, Watchlist

Citation:
L. Sweeney. Privacy-Enhanced Linking. ACM SIGKDD Explorations 7(2) December 2005.
Earlier version available as Carnegie Mellon University, School of Computer Science Technical Report CMU-ISRI-05-136. Pittsburgh: November 2005. (PDF)

Related Publications

• "Privacy Technologies for Homeland Security", Testimony before the Privacy and Integrity Advisory Committee of the Department of Homeland Security (“DHS”), Boston, MA, June 15, 2005. (Testimony and Appendices)

• L. Sweeney. Privacy-Preserving Surveillance Using Selective Revelation. IEEE Intelligent Systems Sept-Oct 2005. (PDF).

• L. Sweeney. AI Technologies to Defeat Identity Theft Vulnerabilities. AAAI Spring Symposium, AI Technologies for Homeland Security, 2005. (PDF).

• L. Sweeney and R. Gross. Mining Images in Publicly-Available Cameras for Homeland Security. AAAI Spring Symposium, AI Technologies for Homeland Security, 2005. (PDF).

• L. Sweeney. Privacy-Preserving Bio-terrorism Surveillance. AAAI Spring Symposium, AI Technologies for Homeland Security, 2005. (Poster).

• L. Sweeney. Towards a Privacy-Preserving Watchlist Solution. AAAI Spring Symposium, AI Technologies for Homeland Security, 2005. (Poster).

• E. Newton, L. Sweeney, and B. Malin. Preserving Privacy by De-identifying Facial Images. IEEE Transactions on Knowledge and Data Engineering, IEEE TKDE, February 2005. Earlier version available as: E. Newton, L. Sweeney, and B. Malin Preserving Privacy by De-identifying Facial Images. Carnegie Mellon University, School of Computer Science, Technical Report, CMU-CS-03-119. Pittsburgh: 2003. (26 pages in PDF).

In the News

• CBS News, Associated Press, March 15, 2004, "Privacy Safeguards Quietly Killed". (text)
• CBS News, Associated Press, November 4, 2002, "Germ Patrol: Like Never Before". (text)

Related Links

• Privacy-preserving Surveillance Project
• Privacy-Preserving Surveillance Using Selective Revelation
• Multi-party computation
• Privacy-preserving Bio-terrorism Surveillance
• Face De-identification
• Identity Angel
• Webcam Surveillance
• The Watchlist Problem
• Social Security numbers
• Datafly
• Scam Spam
• Webcams

• Projects at the Data Privacy Lab
• Data Privacy Laboratory