Differentially Private Distributed Optimization via the Intrinsic Randomness of Zeroth-Order Estimators: Sampling Geometry, Convergence, and Privacy Guarantees

Abstract

Distributed zeroth-order optimization has gained increasing attention in applications where exact gradients are computationally expensive or unavailable. However, the iterative exchange of local decision variables makes distributed networks vulnerable to inference attacks. While differential privacy provides a rigorous framework for privacy protection, conventional approaches generally require additional random noise, which inevitably affects optimization accuracy. In this paper, we investigate whether the randomness inherent in zeroth-order estimation can itself provide privacy protection. We develop a distributed zeroth-order gradient descent method that integrates consensus updates with a ball-uniform two-point gradient estimator. Our theoretical analysis shows that our algorithm attains standard convergence guarantees in both online and offline optimization settings. We then characterize the relevant probability distributions of the associated privacy mechanism, and establish differential privacy guarantees for both the online and offline optimization settings. These results reveal the potential of intrinsic zeroth-order randomness for privacy protection.