Privacy Amplification by Mixing and Diffusion Mechanisms
Published in Advances in Neural Information Processing Systems, 2019
Recommended citation: Balle, Borja, Gilles Barthe, Marco Gaboardi, and Joseph Geumlek (2019). "Privacy amplification by mixing and diffusion mechanisms." Advances in Neural Information Processing Systems, https://papers.nips.cc/paper/9485-privacy-amplification-by-mixing-and-diffusion-mechanisms.pdf
This work studies iterative data analysis using the mathematical theory of diffusion and the mixing of Markov chains. In doing so, the privacy guarantees of the uncertainty added in each iteration can be compounded. It provides a cleaner theoretical framework for the analysis of iterated Noisy Stochastic Gradient Descent, recovering the privacy guarantees already known for that setting, while also showing the flexibility of this new analysis.
This work arose from a Summer internship at Amazon in Cambridge, UK.