Technical Program

Paper Detail

Paper IDP.3.5
Paper Title Privacy Amplification of Iterative Algorithms via Contraction Coefficients
Authors Shahab Asoodeh, Harvard University, United States; Mario Diaz, Universidad Nacional Autonoma de Mexico, Mexico; Flavio P. Calmon, Harvard University, United States
Session P.3: Information Privacy I
Presentation Lecture
Track Cryptography, Security and Privacy
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract We investigate the framework of privacy amplification by iteration, recently proposed by Feldman et al., from an information-theoretic lens. We demonstrate that differential privacy guarantees of iterative mappings can be determined by a direct application of contraction coefficients derived from strong data processing inequalities for f-divergences. In particular, by generalizing the Dobrushin's contraction coefficient for total variation distance to an f-divergence known as E_\gamma-divergence, we derive tighter bounds on the differential privacy parameters of the projected noisy stochastic gradient descent algorithm with hidden intermediate updates.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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