Revealing information while preserving privacy

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Revealing information while preserving privacy (2003)

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by Irit Dinur , Kobbi Nissim

Venue:	In PODS
Citations:	270 - 9 self

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@INPROCEEDINGS{Dinur03revealinginformation,
    author = {Irit Dinur and Kobbi Nissim},
    title = {Revealing information while preserving privacy},
    booktitle = {In PODS},
    year = {2003},
    pages = {202--210},
    publisher = {ACM Press}
}

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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 � i∈q 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 Õ(√n). For time-T bounded adversaries we demonstrate a privacy-preserving access algorithm whose perturbation magnitude is ≈ √ T. 1

Keyphrases

statistical database access algorithm subset sum polynomial reconstruction algorithm main result n-bit string d1 time-t bounded adversary perturbation magnitude strong violation privacy-preserving access algorithm

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