Fair Private Set Intersection with a Semi-trusted Arbiter - Data and Applications Security and Privacy XXVII Access content directly
Conference Papers Year : 2013

Fair Private Set Intersection with a Semi-trusted Arbiter

Changyu Dong
  • Function : Author
  • PersonId : 986114
Liqun Chen
  • Function : Author
  • PersonId : 1004173
Jan Camenisch
  • Function : Author
  • PersonId : 997995
Giovanni Russello
  • Function : Author
  • PersonId : 1004174

Abstract

A private set intersection (PSI) protocol allows two parties to compute the intersection of their input sets privately. Most of the previous PSI protocols only output the result to one party and the other party gets nothing from running the protocols. However, a mutual PSI protocol in which both parties can get the output is highly desirable in many applications. A major obstacle in designing a mutual PSI protocol is how to ensure fairness. In this paper we present the first fair mutual PSI protocol which is efficient and secure. Fairness of the protocol is obtained in an optimistic fashion, i.e. by using an offline third party arbiter. In contrast to many optimistic protocols which require a fully trusted arbiter, in our protocol the arbiter is only required to be semi-trusted, in the sense that we consider it to be a potential threat to both parties’ privacy but believe it will follow the protocol. The arbiter can resolve disputes without knowing any private information belongs to the two parties. This feature is appealing for a PSI protocol in which privacy may be of ultimate importance.
Fichier principal
Vignette du fichier
978-3-642-39256-6_9_Chapter.pdf (652.99 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01490721 , version 1 (15-03-2017)

Licence

Attribution

Identifiers

Cite

Changyu Dong, Liqun Chen, Jan Camenisch, Giovanni Russello. Fair Private Set Intersection with a Semi-trusted Arbiter. 27th Data and Applications Security and Privacy (DBSec), Jul 2013, Newark, NJ, United States. pp.128-144, ⟨10.1007/978-3-642-39256-6_9⟩. ⟨hal-01490721⟩
95 View
91 Download

Altmetric

Share

Gmail Facebook X LinkedIn More