Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154434
Title: Decentralized detection with robust information privacy protection
Authors: Sun, Meng
Tay, Wee Peng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Sun, M. & Tay, W. P. (2020). Decentralized detection with robust information privacy protection. IEEE Transactions On Information Forensics and Security, 15, 85-99. https://dx.doi.org/10.1109/TIFS.2019.2916650
Project: 2017-T1-001-059 (RG20/17)
MOE2018-T2-2- 019
S15-1105-RF-LLF
Journal: IEEE Transactions on Information Forensics and Security
Abstract: We consider a decentralized detection network whose aim is to infer a public hypothesis of interest. However, the raw sensor observations also allow the fusion center to infer private hypotheses that we wish to protect. We consider the case where there are an uncountable number of private hypotheses belonging to an uncertainty set, and develop local privacy mappings at every sensor so that the sanitized sensor information minimizes the Bayes error of detecting the public hypothesis at the fusion center while achieving information privacy for all private hypotheses. We introduce the concept of a most favorable hypothesis (MFH) and show how to find an MFH in the set of private hypotheses. By protecting the information privacy of the MFH, information privacy for every other private hypothesis is also achieved. We provide an iterative algorithm to find the optimal local privacy mappings, and derive some theoretical properties of these privacy mappings. The simulation results demonstrate that our proposed approach allows the fusion center to infer the public hypothesis with low error while protecting information privacy of all the private hypotheses.
URI: https://hdl.handle.net/10356/154434
ISSN: 1556-6013
DOI: 10.1109/TIFS.2019.2916650
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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