Abstract | ||
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AbstractIn emerging information networks, 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 the PPI techniques is how to provide differentiated privacy preservation in the presence of multi-keyword document search. The differentiation is necessary as terms and phrases bear innate differences in their semantic meanings. In this paper, we present ϵ-MPPI, the first work to provide the distributed document search with quantitatively differentiated privacy preservation. In the design of ϵ-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 ϵ-MPPI construction in the multi-domain information network which lacks mutual trusts between domains. Towards a secure ϵ-MPPIconstruction with practically acceptable performance, we proposed to optimize the performance of secure multi-party computations by making a novel use of secret sharing. We implemented the ϵ-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. |
Year | DOI | Venue |
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2015 | 10.1109/TKDE.2015.2407330 | Periodicals |
Keywords | Field | DocType |
Privacy, information networks, secure multi-party computations, indexing, federated databases | Data modeling,Data mining,Secret sharing,Suite,Computer science,Server,Search engine indexing,Information privacy,Optimization problem,Privacy software | Journal |
Volume | Issue | ISSN |
27 | 9 | 1041-4347 |
Citations | PageRank | References |
8 | 0.52 | 17 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
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Yuzhe Tang | 1 | 147 | 21.06 |
Ling Liu | 2 | 2181 | 142.51 |