Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
This addresses privacy concerns in distributed machine learning for applications like healthcare or finance, though it is incremental as it builds on federated learning with specific security enhancements.
The paper tackles privacy leakage in federated expectation maximization for Gaussian mixture models by proposing a fully decentralized solution with subspace perturbation, achieving superior accuracy and privacy levels compared to existing methods.
Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but only intermediate updates. However, federated learning does not always guarantee privacy-preservation as the intermediate updates may also reveal sensitive information. In this paper, we give an explicit information-theoretical analysis of a federated expectation maximization algorithm for Gaussian mixture model and prove that the intermediate updates can cause severe privacy leakage. To address the privacy issue, we propose a fully decentralized privacy-preserving solution, which is able to securely compute the updates in each maximization step. Additionally, we consider two different types of security attacks: the honest-but-curious and eavesdropping adversary models. Numerical validation shows that the proposed approach has superior performance compared to the existing approach in terms of both the accuracy and privacy level.