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Title: A privacy-preserving diffusion strategy over multitask networks
Authors: Wang, Chengcheng
Tay, Wee Peng
Wang, Yuan
Wei, Ye
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Wang, C., Tay, W. P., Wang, Y., & Wei, Y. (2019). A privacy-preserving diffusion strategy over multitask networks. Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7600-7604. doi:10.1109/ICASSP.2019.8682425
Conference: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Abstract: We develop a privacy-preserving distributed strategy over multitask diffusion networks, where each agent is interested in not only improving its local inference performance via in-network cooperation, but also protecting its own individual task against privacy leakage. In the 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 steady-state network mean-square-deviation and an inference privacy constraint. We show that the proposed noise powers are bounded and convergent, which leads to mean-square convergence of the proposed privacy-preserving multitask diffusion scheme. Simulation results demonstrate that the proposed strategy is able to balance the trade-off between estimation accuracy and privacy preservation.
ISBN: 9781479981311
DOI: 10.1109/ICASSP.2019.8682425
Schools: School of Electrical and Electronic Engineering 
Rights: © 2019 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 Conference Papers

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