Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
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Privacy-Preserving Multi-Keywords Search in Information Networks
IEEE Transactions on Knowledge and Data Engineering (TKDE).
Yuzhe Tang, Ling Liu*
Center for Science and Technology, Syracuse
* Georgia Institute of Technology
In emerging multi-domain cloud computing, it is crucially important to provide efficient search on distributed documents while preserving their owners’ privacy, for which privacy preserving indexes or PPI presents a possible solution. An understudied problem for PPI techniques is how to provide differentiated privacy preservation in the face of multi-keyword document search. The differentiation is necessary as terms and phrases bear innate differences in their meanings. In this paper we present ∈-MPPI, the first work on distributed document search with quantitative privacy preservation. In the design of ∈-MPPI, we identified a suite of challenging problems and proposed novel solutions. For one, we formulated the quantitative privacy computation as an optimization problem that strikes a balance between privacy preservation and search efficiency. We also addressed the challenging problem of secure ∈-MPPI construction in the multi-domain network which lacks mutual trusts between the domains. Towards a secure ∈-MPPI construction with practical performance, we proposed techniques for improved performance of secure computations by making a novel use of secret sharing. We implemented the ∈-MPPI construction protocol with a functioning prototype. We conducted extensive experiments to evaluate the prototype’s effectiveness and efficiency based on a real-world dataset.
FULL PAPER: pdf