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dc.contributor.authorWang, Chengchengen_US
dc.contributor.authorTay, Wee Pengen_US
dc.contributor.authorWei, Yeen_US
dc.contributor.authorWang, Yuanen_US
dc.identifier.citationWang, 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.
dc.description.abstractWe 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.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.relation.ispartofIEEE Transactions on Signal Processingen_US
dc.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:
dc.subjectEngineering::Computer science and engineeringen_US
dc.subjectEngineering::Electrical and electronic engineering::Electronic systems::Signal processingen_US
dc.titlePrivacy-preserving distributed projection LMS for linear multitask networksen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.researchCenter for Information Sciences and Systemsen_US
dc.description.versionSubmitted/Accepted versionen_US
dc.subject.keywordsDistributed Strategiesen_US
dc.subject.keywordsMultitask Networksen_US
dc.subject.keywordsInference Privacyen_US
dc.subject.keywordsPrivacy Preservationen_US
dc.subject.keywordsAdditive Noisesen_US
dc.description.acknowledgementThis work was supported in part by the Singapore Ministry of Education Academic Research Fund Tier 2 under Grant MOE2018-T2-2-019, and in part by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre Positioning (IAF-PP) under Grant A19D6a0053.en_US
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