A Dual Perturbation Approach for Differential Private ADMM-Based Distributed Empirical Risk Minimization

The rapid growth of data has raised the importance of privacy-preserving techniques in distributed machine learning. In this paper, we develop a privacy-preserving method to a class of regularized empirical risk minimization (ERM) machine learning problems. We first decentralize the learning algorithm using the alternating direction method of multipliers (ADMM), and propose the method of dual variable perturbation to provide dynamic differential privacy. The mechanism leads to a privacy-preserving algorithm under mild conditions of the convexity and differentiability of the loss function and the regularizer. We study the performance of the algorithm measured by the number of data points required to achieve a bounded error. To design an optimal privacy mechanism, we analyze the fundamental tradeoff between privacy and accuracy, and provide guidelines to choose privacy parameters. Numerical experiments using the real-world database are performed to corroborate the results on the privacy and utility tradeoffs and design.