Bloom Filter Bootstrap: Privacy-Preserving Estimation of the Size of an Intersection - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

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

Hiroaki Kikuchi
  • Function : Author
  • PersonId : 1004130
Jun Sakuma
  • Function : Author
  • PersonId : 1004131

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 χ2-test can be performed without disclosing the contents of the two confidential databases.
Fichier principal
Vignette du fichier
978-3-642-39256-6_10_Chapter.pdf (165.75 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01490702 , version 1 (15-03-2017)

Licence

Attribution

Identifiers

Cite

Hiroaki Kikuchi, Jun Sakuma. Bloom Filter Bootstrap: Privacy-Preserving Estimation of the Size of an Intersection. 27th Data and Applications Security and Privacy (DBSec), Jul 2013, Newark, NJ, United States. pp.145-163, ⟨10.1007/978-3-642-39256-6_10⟩. ⟨hal-01490702⟩
33 View
132 Download

Altmetric

Share

Gmail Facebook X LinkedIn More