Our Data, Ourselves: Privacy Via Distributed Noise Generation

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 P 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 verifia...

• Type: Conference
• Year: 2006
• Where: EUROCRYPT
• Authors: Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, Moni Naor
• Keywords: Cryptology, Distributed Implementation, EUROCRYPT 2006, Noise Generation, Random Noise