Privacy-Preserving Distributed Projection LMS for Linear Multitask Networks

Abstract

We develop a privacy-preserving distributed projection least mean squares (LMS) strategy over linear multitask networks, where agents' local parameters of interest or tasks are linearly related. Each agent is interested in not only improving its local inference performance via in-network cooperation with neighboring agents, but also protecting its own individual task against privacy leakage. In our proposed strategy, at each time instant, each agent sends a noisy estimate, which is its local intermediate estimate corrupted by a zero-mean additive noise, to its neighboring agents. We derive a sufficient condition to determine the amount of noise to add to each agent's intermediate estimate to achieve an optimal trade-off between the network mean-square-deviation and an inference privacy constraint. We propose a distributed and adaptive strategy to compute the additive noise powers, and study the mean and mean-square behaviors and privacy-preserving performance of the proposed strategy. Simulation results demonstrate that our strategy is able to balance the trade-off between estimation accuracy and privacy preservation.

References in corpus (15)

• Multitask Diffusion Adaptation over Networks
• LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy
• Diffusion LMS over Multitask Networks
• Diffusion Adaptation over Networks under Imperfect Information Exchange and Non-stationary Data
• Distributed Pareto Optimization via Diffusion Strategies
• Distributed Clustering and Learning Over Networks
• Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation
• Proximal Multitask Learning over Networks with Sparsity-inducing Coregularization
• Diffusion LMS for Multitask Problems with Local Linear Equality Constraints
• Multitask diffusion adaptation over networks with common latent representations
• Robust Decentralized Detection and Social Learning in Tandem Networks
• Whose Opinion to follow in Multihypothesis Social Learning? A Large Deviations Perspective
• Distributed Local Linear Parameter Estimation using Gaussian SPAWN
• Multi-hop Diffusion LMS for Energy-constrained Distributed Estimation
• A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation