Title:
𝜖-PPI: Searching Information Networks with Quantitative Privacy Guarantee
𝜖-PPI: Searching Information Networks with Quantitative Privacy Guarantee
Author(s)
Tang, Yuzhe
Liu, Ling
Iyengar, Arun
Liu, Ling
Iyengar, Arun
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Abstract
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
𝜖-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 𝜖-PPI. The
proposed 𝜖-PPI construction protocol is the first without
any trusted third party and/or trust relationship between
providers. We have implemented our 𝜖-PPI construction
protocol by using generic MPC techniques (secure multiparty
computation) and optimized the performance to a
practical level by minimizing the costly MPC computation
part.
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Date Issued
2014
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Resource Type
Text
Resource Subtype
Technical Report