Privacy-Preserving Fully Distributed Gaussian Process Regression

Abstract

Although distributed Gaussian process regression (GPR) enables multiple agents to jointly learn a model of the target function, its collaborative nature poses a risk of private data leakage. To address this, we propose a privacy-preserving fully distributed GPR protocol based on secure multi-party computation, which hides each agent's individual contribution from semi-honest coalitions of bounded size, beyond what is implied by the aggregated value. Building upon a secure distributed average consensus algorithm, it guarantees that each agent's local model practically converges to the same global model obtained by the standard distributed GPR. Formal privacy guarantees are established within the simulation based security paradigm. The protocol is further extended to privacy-preserving optimization of kernel hyperparameters, which is critical yet often overlooked in the literature. Experimental results demonstrate the effectiveness and practical applicability of the proposed method.

12 pages, 3 figures, 1 table, revised version submitted to IEEE Transactions on Control of Network Systems