ε-PPI: Locator Service in Information Networks with Personalized Privacy Preservation

In emerging information networks, having a privacy preserving index (or PPI) is critically important for locating information of interest for data sharing across autonomous providers while preserving privacy. An understudied problem for PPI techniques is how to provide controllable privacy preservation, given the innate difference of privacy concerns regarding different data owners. In this paper we present a personalized privacy preserving index, coined epsilon-PPI, which guarantees quantitative privacy preservation differentiated by personal identities. We devise a new common-identity attack that breaks existing PPI's and propose an identity-mixing protocol against the attack in epsilon-PPI. The proposed epsilon-PPI construction protocol is the first without any trusted third party and/or trust relationships between providers. We have implemented our epsilon-PPI construction protocol by using generic MPC techniques (secure multi-party computation) and optimized the performance to a practical level by minimizing the expensive MPC part.