Scalable and secure Private Set intersection for big data

Changhee Hahn, Junbeom Hur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we investigate Private Set Intersection (PSI) schemes that can be used to output intersection data between a client and a server in a way that only the client learns the output at the end of their joint computation. Recently, Dong et al. proposed a Bloom filter-based PSI scheme for big data. We show that a malicious client is able to learn not only the intersection but other part of the server's set in Dong et al.'s scheme. This can be delivered by submitting arbitrary Bloom filters as inputs. To this end, we suggest a Merkle tree-based countermeasure. It prevents malicious clients from learning any part of the servers set except the intersection. The security and performance analysis shows that our scheme is secure against the malicious client with a minor efficiency degradation.

Original languageEnglish
Title of host publication2016 International Conference on Big Data and Smart Computing, BigComp 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages285-288
Number of pages4
ISBN (Electronic)9781467387965
DOIs
Publication statusPublished - 2016 Jan 1
EventInternational Conference on Big Data and Smart Computing, BigComp 2016 - Hong Kong, China
Duration: 2016 Jan 182016 Jan 20

Publication series

Name2016 International Conference on Big Data and Smart Computing, BigComp 2016

Other

OtherInternational Conference on Big Data and Smart Computing, BigComp 2016
CountryChina
CityHong Kong
Period16/1/1816/1/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Hahn, C., & Hur, J. (2016). Scalable and secure Private Set intersection for big data. In 2016 International Conference on Big Data and Smart Computing, BigComp 2016 (pp. 285-288). [7425929] (2016 International Conference on Big Data and Smart Computing, BigComp 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2016.7425929