Abstract:We model the dynamics of privacy loss in Langevin diffusion and extend it to the noisy gradient descent algorithm: we compute a tight bound on Rényi differential privacy and the rate of its change throughout the learning process. We prove that the privacy loss converges exponentially fast. This significantly improves the prior privacy analysis of differentially private (stochastic) gradient descent algorithms, where (Rényi) privacy loss constantly increases over the training iterations. Unlike composition-base… Show more
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