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Bloom Filter Bootstrap: Privacy-Preserving Estimation of the Size of an Intersection

  • Hiroaki Kikuchi
  • Jun Sakuma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7964)

Abstract

This paper proposes a new privacy-preserving scheme for estimating the size of the intersection of two given secret subsets. Given the inner product of two Bloom filters (BFs) of the given sets, the proposed scheme applies Bayesian estimation under assumption of beta distribution for an a priori probability of the size to be estimated. The BF retains the communication complexity and the Bayesian estimation improves the estimation accuracy.

An possible application of the proposed protocol is an epidemiological datasets regarding two attributes, Helicobactor pylori infection and stomach cancer. Assuming information related to Helicobactor Pylori infection and stomach cancer are separately collected, the protocol demonstrates that a χ 2-test can be performed without disclosing the contents of the two confidential databases.

Keywords

Hash Function Beta Distribution Stomach Cancer Bayesian Estimation Bloom Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Hiroaki Kikuchi
    • 1
  • Jun Sakuma
    • 2
  1. 1.Department of Frontier Media Science, School of Interdisciplinary Mathematical SciencesMeiji UniversityNakano KuJapan
  2. 2.Graduate School of SIE, Computer Science DepartmentUniversity of TsukubaTsukubaJapan

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