Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146327
Title: Arbitrarily strong utility-privacy tradeoff in multi-agent systems
Authors: Wang, Chong Xiao
Song, Yang
Tay, Wee Peng 
Keywords: Engineering
Issue Date: 2020
Source: Wang, C. X., Song, Y., & Tay, W. P. (2020). Arbitrarily strong utility-privacy tradeoff in multi-agent systems. IEEE Transactions on Information Forensics and Security, 16, 671-684. doi:10.1109/TIFS.2020.3016835
Journal: IEEE Transactions on Information Forensics and Security
Abstract: Each agent in a network makes a local observation that is linearly related to a set of public and private parameters. The agents send their observations to a fusion center to allow it to estimate the public parameters. To prevent leakage of the private parameters, each agent first sanitizes its local observation using a local privacy mechanism before transmitting it to the fusion center. We investigate the utility privacy tradeoff in terms of the Cramér-Rao lower bounds for estimating the public and private parameters. We study the class of privacy mechanisms given by linear compression and noise perturbation, and derive necessary and sufficient conditions for achieving arbitrarily strong utility privacy tradeoff in a multi-agent system for both the cases where prior information is available and unavailable, respectively. We also provide a method to find the maximum estimation privacy achievable without compromising the utility and propose an alternating algorithm to optimize the utility-privacy tradeoff in the case where arbitrarily strong utility-privacy tradeoff is not achievable.
URI: https://hdl.handle.net/10356/146327
ISSN: 1556-6013
DOI: 10.1109/TIFS.2020.3016835
Rights: © 2020 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/TIFS.2020.3016835
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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