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Title: Privacy-preserving distributed projection LMS for linear multitask networks
Authors: Wang, Chengcheng
Tay, Wee Peng
Wei, Ye
Wang, Yuan
Keywords: Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2021
Source: Wang, C., Tay, W. P., Wei, Y. & Wang, Y. (2021). Privacy-preserving distributed projection LMS for linear multitask networks. IEEE Transactions On Signal Processing, 69, 6530-6545.
Project: MOE2018-T2-2-019 
Journal: IEEE Transactions on Signal Processing
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.
ISSN: 1053-587X
DOI: 10.1109/TSP.2021.3126929
Schools: School of Electrical and Electronic Engineering 
Research Centres: Center for Information Sciences and Systems
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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