-PPI: Searching Information Networks with Quantitative Privacy Guarantee

In information sharing networks, having a privacy preserving index (or PPI) is critically important for providing efficient search on access controlled content across distributed providers while preserving privacy. An understudied problem for PPI techniques is how to provide controllable privacy preservation, given the innate difference of privacy of the different content and providers. In this paper we present a configurable privacy preserving index, coined ǫ-PPI, which allows for quantitative privacy protection levels on fine-grained data units. We devise a new common-identity attack that breaks existing PPI’s and propose an identity-mixing protocol against the attack in ǫ-PPI. The proposed ǫ-PPI construction protocol is the first without any trusted third party and/or trust relationship between providers. We have implemented our ǫ-PPI construction protocol by using generic MPC techniques (secure multi-party computation) and optimized the performance to a practical level by minimizing the costly MPC computation part.