Privacy-preserving distributed projection LMS for linear multitask networks

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 cooper...

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Main Authors: Wang, Chengcheng, Tay, Wee Peng, Wei, Ye, Wang, Yuan
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/156347
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1563472022-04-26T04:03:57Z Privacy-preserving distributed projection LMS for linear multitask networks Wang, Chengcheng Tay, Wee Peng Wei, Ye Wang, Yuan School of Electrical and Electronic Engineering Center for Information Sciences and Systems Engineering::Computer science and engineering Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Distributed Strategies Multitask Networks Inference Privacy Privacy Preservation Additive Noises 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. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Submitted/Accepted version This 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. 2022-04-17T09:03:04Z 2022-04-17T09:03:04Z 2021 Journal Article 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. https://dx.doi.org/10.1109/TSP.2021.3126929 1053-587X https://hdl.handle.net/10356/156347 10.1109/TSP.2021.3126929 2-s2.0-85119438486 69 6530 6545 en MOE2018-T2-2-019 A19D6a0053 IEEE Transactions on Signal Processing © 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: https://doi.org/10.1109/TSP.2021.3126929. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Distributed Strategies
Multitask Networks
Inference Privacy
Privacy Preservation
Additive Noises
spellingShingle Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Distributed Strategies
Multitask Networks
Inference Privacy
Privacy Preservation
Additive Noises
Wang, Chengcheng
Tay, Wee Peng
Wei, Ye
Wang, Yuan
Privacy-preserving distributed projection LMS for linear multitask networks
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Wang, Chengcheng
Tay, Wee Peng
Wei, Ye
Wang, Yuan
format Article
author Wang, Chengcheng
Tay, Wee Peng
Wei, Ye
Wang, Yuan
author_sort Wang, Chengcheng
title Privacy-preserving distributed projection LMS for linear multitask networks
title_short Privacy-preserving distributed projection LMS for linear multitask networks
title_full Privacy-preserving distributed projection LMS for linear multitask networks
title_fullStr Privacy-preserving distributed projection LMS for linear multitask networks
title_full_unstemmed Privacy-preserving distributed projection LMS for linear multitask networks
title_sort privacy-preserving distributed projection lms for linear multitask networks
publishDate 2022
url https://hdl.handle.net/10356/156347
_version_ 1731235704549146624