Differentially Private Network Training under Hidden State Assumption

Ding Chen, Chen Liu

Keywords: Differential Privacy, Coordinate Descent

Abstract:

We present a novel approach called differentially private stochastic block coordinate descent (DP-SBCD) for training neural networks with provable guarantees of differential privacy under the hidden state assumption. Our methodology regards neural networks as optimization problems and decomposes the training process of the neural network into sub-problems, each corresponding to the training of a specific layer. By doing so, we extend the analysis of differential privacy under the hidden state assumption to encompass non-convex problems and algorithms employing proximal gradient descent. Furthermore, in contrast to existing methods, we adopt a novel approach by utilizing calibrated noise sampled from adaptive distributions, yielding improved empirical trade-offs between utility and privacy.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 6700