Calibrating noise to sensitivity in private data analysis

Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith

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

2157 Scopus citations

Abstract

We continue a line of research initiated in [10, 11] on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function / mapping databases to reals, the so-called true answer is the result of applying / to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = ∑ i g(x i), where x i denotes the ith row of the database and g maps database rows to [0, 1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f. Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.

Original languageEnglish (US)
Title of host publicationTheory of Cryptography
Subtitle of host publicationThird Theory of Cryptography Conference, TCC 2006, Proceedings
Pages265-284
Number of pages20
DOIs
StatePublished - 2006
Event3rd Theory of Cryptography Conference, TCC 2006 - New York, NY, United States
Duration: Mar 4 2006Mar 7 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3876 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd Theory of Cryptography Conference, TCC 2006
CountryUnited States
CityNew York, NY
Period3/4/063/7/06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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