Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan
Abstract
We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms. Specifically, we provide a tight upper bound on the Renyi Differential Privacy (RDP) [Mironov, 2017] parameters for algorithms that: (1) subsample the dataset, and then (2) apply a randomized mechanism M to the subsample, in terms of the RDP parameters of M and the subsampling probability parameter.Our results generalize the moments accounting technique, developed by [Abadi et al. 2016] for the Gaussian mechanism, to any subsampled RDP mechanism.
Topics & Concepts
Differential privacyGaussianMechanism (biology)Computer scienceDifferential (mechanical device)Moment (physics)Artificial intelligenceAlgorithmMathematicsPhysicsQuantum mechanicsThermodynamicsPrivacy-Preserving Technologies in DataComplexity and Algorithms in GraphsStochastic Gradient Optimization Techniques