Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138203
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
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.
URI: https://hdl.handle.net/10356/138203
ISBN: 9781479981311
DOI: 10.1109/ICASSP.2019.8682425
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: https://doi.org/10.1109/ICASSP.2019.8682425
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

Files in This Item:
File Description SizeFormat 
ICASSP_final.pdf311.71 kBAdobe PDFView/Open

SCOPUSTM   
Citations 50

3
Updated on Feb 5, 2023

Page view(s)

185
Updated on Feb 7, 2023

Download(s) 50

77
Updated on Feb 7, 2023

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.