Private algorithms for the protected in social network search
Abstract
Motivated by tensions between data privacy for individual citizens, and societal priorities such as counterterrorism, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the “targeted” subpopulation). Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 11, 2016
- Source ID
- 10.1073/pnas.1510612113
Entities
People
- Aaron Roth
- Grigory Yaroslavtsev
- Michael Kearns
- Zhiwei Steven Wu
Organizations
- Army Research Office
- National Science Foundation
- University of Pennsylvania