Efficient Noise Generation to Achieve Differential Privacy with Applications to Secure Multiparty Computation

Reo Eriguchi, Atsunori Ichikawa, Noboru Kunihiro, Koji Nuida

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

Abstract

This paper studies the problem of constructing secure multiparty computation protocols whose outputs satisfy differential privacy. We first provide a general framework for multiparty protocols generating shares of noise drawn from distributions capable of achieving differential privacy. Then, using this framework, we propose two kinds of protocols based on secret sharing. The first one is a constant-round protocol which enables parties to jointly generate shares of noise drawn from the discrete Laplace distribution. This protocol always outputs shares of noise while the previously known protocol fails with non-zero probability. The second protocol allows the parties to non-interactively obtain shares of noise following the binomial distribution by predistributing keys for pseudorandom functions in the setup phase. As a result, the parties can compute a share of noise enough to provide the computational analogue of ϵ -differential privacy with communication complexity independent of ϵ. It is much more efficient than the previous protocols which require communication complexity proportional to ϵ- 2 to achieve (information-theoretic) (ϵ, δ) -differential privacy for some δ> 0.

Original languageEnglish
Title of host publicationFinancial Cryptography and Data Security - 25th International Conference, FC 2021, Revised Selected Papers
EditorsNikita Borisov, Claudia Diaz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages271-290
Number of pages20
ISBN (Print)9783662643211
DOIs
Publication statusPublished - 2021
Event25th International Conference on Financial Cryptography and Data Security, FC 2021 - Virtual, Online
Duration: Mar 1 2021Mar 5 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12674 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Financial Cryptography and Data Security, FC 2021
CityVirtual, Online
Period3/1/213/5/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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