O-PSI: delegated private set intersection on outsourced datasets

Aydin Abadi, Sortirios Terzis, Changyu Dong

Research output: Contribution to conferencePaper

25 Citations (Scopus)
134 Downloads (Pure)

Abstract

Private set intersection (PSI) has a wide range of applications such as privacy-preserving data mining. With the advent of cloud computing it is now desirable to take advantage of the storage and computation capabilities of the cloud to outsource datasets and delegate PSI computation. In this paper we design OPSI, a protocol for delegated private set intersection on outsourced datasets based on a novel point-value polynomial representation. Our protocol allows multiple clients to independently prepare and upload their private datasets to a server, and then ask the server to calculate their intersection. The protocol ensures that intersections can only be calculated with the permission of all clients and that datasets and results remain completely confidential from the server. Once datasets are outsourced, the protocol supports an unlimited number of intersections with no need to download them or prepare them again for computation. Our protocol is efficient and has computation and communication costs linear to the cardinality of the datasets. We also provide a formal security analysis of the protocol.
Original languageEnglish
Pages3-17
Number of pages15
DOIs
Publication statusPublished - 26 May 2015
EventInternational Conference on ICT Systems Security and Privacy Protection - Hamburg, Germany
Duration: 26 May 201528 May 2015
https://ifipsec.org/2015/

Conference

ConferenceInternational Conference on ICT Systems Security and Privacy Protection
Abbreviated titleIFIP SEC 2015
CountryGermany
CityHamburg
Period26/05/1528/05/15
Internet address

Keywords

  • private set intersection
  • privacy-preserving data mining
  • outsource datasets

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    Abadi, A., Terzis, S., & Dong, C. (2015). O-PSI: delegated private set intersection on outsourced datasets. 3-17. Paper presented at International Conference on ICT Systems Security and Privacy Protection, Hamburg, Germany. https://doi.org/10.1007/978-3-319-18467-8_1