Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146327
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dc.contributor.authorWang, Chong Xiaoen_US
dc.contributor.authorSong, Yangen_US
dc.contributor.authorTay, Wee Pengen_US
dc.date.accessioned2021-02-09T07:09:00Z-
dc.date.available2021-02-09T07:09:00Z-
dc.date.issued2020-
dc.identifier.citationWang, 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.2020.3016835en_US
dc.identifier.issn1556-6013en_US
dc.identifier.urihttps://hdl.handle.net/10356/146327-
dc.description.abstractEach 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.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Supercomputing Centre (NSCC) Singaporeen_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Information Forensics and Securityen_US
dc.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.2020.3016835en_US
dc.subjectEngineeringen_US
dc.titleArbitrarily strong utility-privacy tradeoff in multi-agent systemsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.researchCentre for Information Sciences and Systemsen_US
dc.identifier.doi10.1109/TIFS.2020.3016835-
dc.description.versionAccepted versionen_US
dc.identifier.volume16en_US
dc.identifier.spage671en_US
dc.identifier.epage684en_US
dc.subject.keywordsInference Privacyen_US
dc.subject.keywordsCramér-Rao Lower Bounden_US
dc.description.acknowledgementThis work was supported in part by the Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2018-T2-2-019 and by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund – Pre Positioning (IAF-PP) (Grant No. A19D6a0053). The computational work for this article was partially performed on resources of the National Supercomputing Centre, Singapore (https://www.nscc.sg).en_US
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