Calibrating noise to sensitivity in private data analysis

DMCA

Calibrating noise to sensitivity in private data analysis (2006)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Cynthia Dwork , Frank Mcsherry , Kobbi Nissim , Adam Smith

Venue:	In Proceedings of the 3rd Theory of Cryptography Conference
Citations:	619 - 56 self

BibTeX

@INPROCEEDINGS{Dwork06calibratingnoise,
    author = {Cynthia Dwork and Frank Mcsherry and Kobbi Nissim and Adam Smith},
    title = {Calibrating noise to sensitivity in private data analysis},
    booktitle = {In Proceedings of the 3rd Theory of Cryptography Conference},
    year = {2006},
    pages = {265--284},
    publisher = {Springer}
}

OpenURL

Abstract

Abstract. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f 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 =P i g(xi), where xi denotes the ith row of the database and g maps data-base 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. 1 Introduction We continue a line of research initiated in [10, 11] on privacy in statistical data-bases. A statistic is a quantity computed from a sample. Intuitively, if the database is a representative sample of an underlying population, the goal ofa privacy-preserving statistical database is to enable the user to learn properties of the population as a whole while protecting the privacy of the individualcontributors.

Keyphrases

private data analysis true answer ith row roughly speaking map data-base row interactive sanitization mechanism clean characterization statistical database previous work new analysis representative sample query function mapping privacy-preserving statistical database trusted server single argument sensitive information statistical data-bases random noise several particular application separation result underlying population general function first step standard deviation so-called true answer noisy sum

DMCA