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Privacy Amplification by Mixing and Diffusion Mechanisms
Borja Balle · Gilles Barthe · Marco Gaboardi · Joseph Geumlek

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #162

A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output. In this paper we investigate under what conditions stochastic post-processing can amplify the privacy of a mechanism. By interpreting post-processing as the application of a Markov operator, we first give a series of amplification results in terms of uniform mixing properties of the Markov process defined by said operator. Next we provide amplification bounds in terms of coupling arguments which can be applied in cases where uniform mixing is not available. Finally, we introduce a new family of mechanisms based on diffusion processes which are closed under post-processing, and analyze their privacy via a novel heat flow argument. On the applied side, we generalize the analysis of "privacy amplification by iteration" in Noisy SGD and show it admits an exponential improvement in the strongly convex case, and study a mechanism based on the Ornstein–Uhlenbeck diffusion process which contains the Gaussian mechanism with optimal post-processing on bounded inputs as a special case.

Author Information

Borja Balle (Amazon)
Gilles Barthe (Max Planck Institute)
Marco Gaboardi (Univeristy at Buffalo)
Joseph Geumlek (University of California, San Diego)

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