Title:
𝜖-PPI: Searching Information Networks with Quantitative Privacy Guarantee

dc.contributor.author Tang, Yuzhe
dc.contributor.author Liu, Ling
dc.contributor.author Iyengar, Arun
dc.contributor.corporatename Georgia Institute of Technology. Center for Experimental Research in Computer Systems en_US
dc.contributor.corporatename Georgia Institute of Technology. College of Computing en_US
dc.contributor.corporatename Thomas J. Watson IBM Research Center en_US
dc.date.accessioned 2015-06-09T17:42:30Z
dc.date.available 2015-06-09T17:42:30Z
dc.date.issued 2014
dc.description.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. en_US
dc.embargo.terms null en_US
dc.identifier.uri http://hdl.handle.net/1853/53630
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries CERCS ; GIT-CERCS-14-02 en_US
dc.subject Access controlled content en_US
dc.subject Common identity en_US
dc.subject Multiparty computation en_US
dc.subject Privacy en_US
dc.subject Privacy preserving index en_US
dc.title 𝜖-PPI: Searching Information Networks with Quantitative Privacy Guarantee en_US
dc.type Text
dc.type.genre Technical Report
dspace.entity.type Publication
local.contributor.author Liu, Ling
local.contributor.corporatename Center for Experimental Research in Computer Systems
local.relation.ispartofseries CERCS Technical Report Series
relation.isAuthorOfPublication 96391b98-ac42-4e2c-93ee-79a5e16c2dfb
relation.isOrgUnitOfPublication 1dd858c0-be27-47fd-873d-208407cf0794
relation.isSeriesOfPublication bc21f6b3-4b86-4b92-8b66-d65d59e12c54
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