Smooth sensitivity and sampling in private data analysis

Kobbi Nissim, Sofya Raskhodnikova, Adam Smith

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

374 Scopus citations

Abstract

We introduce a new, generic framework for private data analysis.The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains.Our framework allows one to release functions f of the data withinstance-based additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also bythe database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smoothsensitivity of f on the database x - - a measure of variabilityof f in the neighborhood of the instance x. The new frameworkgreatly expands the applicability of output perturbation, a technique for protecting individuals' privacy by adding a smallamount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect of instance-basednoise in the context of data privacy. Our framework raises many interesting algorithmic questions. Namely,to apply the framework one must compute or approximate the smoothsensitivity of f on x. We show how to do this efficiently for several different functions, including the median and the cost ofthe minimum spanning tree. We also give a generic procedure based on sampling that allows one to release f(x) accurately on manydatabases x. This procedure is applicable even when no efficient algorithm for approximating smooth sensitivity of f is known orwhen f is given as a black box. We illustrate the procedure by applying it to k-SED (k-means) clustering and learning mixtures of Gaussians.

Original languageEnglish (US)
Title of host publicationSTOC'07
Subtitle of host publicationProceedings of the 39th Annual ACM Symposium on Theory of Computing
Pages75-84
Number of pages10
DOIs
StatePublished - Oct 30 2007
EventSTOC'07: 39th Annual ACM Symposium on Theory of Computing - San Diego, CA, United States
Duration: Jun 11 2007Jun 13 2007

Publication series

NameProceedings of the Annual ACM Symposium on Theory of Computing
ISSN (Print)0737-8017

Other

OtherSTOC'07: 39th Annual ACM Symposium on Theory of Computing
CountryUnited States
CitySan Diego, CA
Period6/11/076/13/07

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All Science Journal Classification (ASJC) codes

  • Software

Cite this

Nissim, K., Raskhodnikova, S., & Smith, A. (2007). Smooth sensitivity and sampling in private data analysis. In STOC'07: Proceedings of the 39th Annual ACM Symposium on Theory of Computing (pp. 75-84). (Proceedings of the Annual ACM Symposium on Theory of Computing). https://doi.org/10.1145/1250790.1250803