Turning privacy leaks into floods: surreptitious discovery of social network friendships and other sensitive binary attribute vectors

  • Authors:

  • Arthur U. Asuncion;Michael T. Goodrich

  • Affiliations:

  • University of California, Irvine, Irvine, CA, USA;University of California, Irvine, Irvine, CA, USA

  • Venue:
  • Proceedings of the 9th annual ACM workshop on Privacy in the electronic society
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

We study methods for attacking the privacy of social networking sites, collaborative filtering sites, databases of genetic signatures, and other data sets that can be represented as vectors of binary relationships. Our methods are based on reductions to nonadaptive group testing, which implies that our methods can exploit a minimal amount of privacy leakage, such as contained in a single bit that indicates if two people in a social network have a friend in common or not. We analyze our methods for turning such privacy leaks into floods using theoretical characterizations as well as experimental tests. Our empirical analyses are based on experiments involving privacy attacks on the social networking sites Facebook and LiveJournal, a database of mitochondrial DNA, a power grid network, and the movie-rating database released as a part of the Netflix Prize contest. For instance, with respect to Facebook, our analysis shows that it is effectively possible to break the privacy of members who restrict their friends lists to friends-of-friends.