Distributed private data analysis: Simultaneously solving how and what

Amos Beimel, Kobbi Nissim, Eran Omri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

110 Scopus citations

Abstract

We examine the combination of two directions in the field of privacy concerning computations over distributed private inputs - secure function evaluation (SFE) and differential privacy. While in both the goal is to privately evaluate some function of the individual inputs, the privacy requirements are significantly different. The general feasibility results for SFE suggest a natural paradigm for implementing differentially private analyses distributively: First choose what to compute, i.e., a differentially private analysis; Then decide how to compute it, i.e., construct an SFE protocol for this analysis. We initiate an examination whether there are advantages to a paradigm where both decisions are made simultaneously. In particular, we investigate under which accuracy requirements it is beneficial to adapt this paradigm for computing a collection of functions including Binary Sum, Gap Threshold, and Approximate Median queries. Our results yield new separations between the local and global models of computations for private data analysis.

Original languageEnglish
Title of host publicationAdvances in Cryptology - CRYPTO 2008 - 28th Annual International Cryptology Conference, Proceedings
Pages451-468
Number of pages18
DOIs
StatePublished - 2008
Externally publishedYes
Event28th Annual International Cryptology Conference, CRYPTO 2008 - Santa Barbara, CA, United States
Duration: 17 Aug 200821 Aug 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5157 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th Annual International Cryptology Conference, CRYPTO 2008
Country/TerritoryUnited States
CitySanta Barbara, CA
Period17/08/0821/08/08

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