Our Data, Ourselves: Privacy via Distributed Noise Generation

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Our Data, Ourselves: Privacy via Distributed Noise Generation (2006)

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by Cynthia Dwork , Krishnaram Kenthapadi , Frank Mcsherry , Ilya Mironov , Moni Naor

Venue:	In EUROCRYPT
Citations:	152 - 15 self

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@INPROCEEDINGS{Dwork06ourdata,,
    author = {Cynthia Dwork and Krishnaram Kenthapadi and Frank Mcsherry and Ilya Mironov and Moni Naor},
    title = {Our Data, Ourselves: Privacy via Distributed Noise Generation},
    booktitle = {In EUROCRYPT},
    year = {2006},
    pages = {486--503},
    publisher = {Springer}
}

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Abstract

Abstract. In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributed implementation of the privacy-preserving statistical databases described in recent papers [14,4,13]. In these databases, privacy is obtained by perturbing the true answer to a database query by the addition of a small amount of Gaussian or exponentially distributed random noise. The computational power of evenasimple form of these databases, when the queryis just of the form È i f(di), that is, the sum over all rows i in the database of a function f applied to the data in row i, has been demonstrated in [4]. A distributed implementation eliminates the need for a trusted database administrator. The results for noise generation are of independent interest. The generation of Gaussian noise introduces a technique for distributing shares of many unbiased coins with fewer executions of verifiable secret sharing than would be needed using previous approaches (reduced by afactorofn). The generation of exponentially distributed noise uses two shallow circuits: one for generating many arbitrarily but identically biased coins at an amortized cost of two unbiased random bits apiece, independent of the bias, and the other to combine bits of appropriate biases to obtain an exponential distribution. 1

Keyphrases

distributed noise generation noise generation random noise distributed implementation many unbiased coin independent interest evenasimple form malicious participant true answer verifiable secret sharing shallow circuit efficient distributed protocol gaussian noise privacy-preserving statistical database appropriate bias database query unbiased random bit amortized cost noise us small amount computational power exponential distribution previous approach recent paper trusted database administrator

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