Our data, ourselves: privacy via distributed noise generation
Cynthia Dwork,Krishnaram Kenthapadi,Frank McSherry,Ilya Mironov,Moni Naor +4 more
- Vol. 4004, pp 486-503
TLDR
In this paper, a distributed protocol for generating shares of random noise, secure against malicious participants, was proposed, where the purpose of the noise generation is to create a distributed implementation of the privacy-preserving statistical databases described in recent papers.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 even a simple form of these databases, when the query is just of the form ∑if(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 a factor of n). 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.read more
Citations
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Secure sublinear time differentially private median computation
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TL;DR: In this paper , the authors studied the relationship between adversarial robustness and differential privacy in high-dimensional algorithmic statistics and gave the first black-box reduction from privacy to robustness which can produce private estimators with optimal tradeoffs among sample complexity, accuracy, and privacy for a wide range of fundamental highdimensional parameter estimation problems, including mean and covariance estimation.
References
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Book ChapterDOI
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Calibrating noise to sensitivity in private data analysis
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