Revealing Information while Preserving Privacy

Irit Dinur, Kobbi Nissim

Research output: Contribution to conferencePaperpeer-review

493 Scopus citations

Abstract

We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an n-bit string d1, .., dn, with a query being a subset q ⊆ [n] to be answered by Σiq di. Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset sums. Applying this reconstruction algorithm to statistical databases we show that in order to achieve privacy one has to add perturbation of magnitude Ω(√n). That is, smaller perturbation always results in a strong violation of privacy. We show that this result is tight by exemplifying access algorithms for statistical databases that preserve privacy while adding perturbation of magnitude Ō(√n). For time-T bounded adversaries we demonstrate a privacy-preserving access algorithm whose perturbation magnitude is ≈ √T.

Original languageEnglish
Pages202-210
Number of pages9
DOIs
StatePublished - 1 Jan 2003
Externally publishedYes
EventTwenty second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2003 - San Diego, CA, United States
Duration: 9 Jun 200311 Jun 2003

Conference

ConferenceTwenty second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2003
Country/TerritoryUnited States
CitySan Diego, CA
Period9/06/0311/06/03

Keywords

  • Data Reconstruction
  • Integrity and Security
  • Subset-sums with noise

Fingerprint

Dive into the research topics of 'Revealing Information while Preserving Privacy'. Together they form a unique fingerprint.

Cite this