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GIT-CERCS-14-02
Yuzhe Tang, Ling Liu, Arun Iyengar,
e-PPI: Searching Information Networks with Quantitative Privacy Guarantee

In information sharing networks, having a privacy pre- serving index (or PPI) is critically important for providing efficient search on access controlled content across dis- tributed providers while preserving privacy. An understud- ied problem for PPI techniques is how to provide control- lable 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 e-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 e-PPI. The proposed e-PPI construction protocol is the first without any trusted third party and/or trust relationship between providers. We have implemented our e-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.

 

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