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dc.contributor.authorSun, Mengen_US
dc.contributor.authorTay, Wee Pengen_US
dc.identifier.citationSun, M. & Tay, W. P. (2020). On the relationship between inference and data privacy in decentralized IoT networks. IEEE Transactions On Information Forensics and Security, 15, 852-866.
dc.description.abstractIn a decentralized Internet of Things (IoT) network, a fusion center receives information from multiple sensors to infer a public hypothesis of interest. To prevent the fusion center from abusing the sensor information, each sensor sanitizes its local observation using a local privacy mapping, which is designed to achieve both inference privacy of a private hypothesis and data privacy of the sensor raw observations. Various inference and data privacy metrics have been proposed in the literature. We introduce the concept of privacy implication (with vanishing budget) to study the relationships between these privacy metrics. We propose an optimization framework in which both local differential privacy (data privacy) and information privacy (inference privacy) metrics are incorporated. In the parametric case where sensor observations' distributions are known a priori, we propose a two-stage local privacy mapping at each sensor, and show that such an architecture is able to achieve information privacy and local differential privacy to within the predefined budgets. For the nonparametric case where sensor distributions are unknown, we adopt an empirical optimization approach. Simulation and experiment results demonstrate that our proposed approaches allow the fusion center to accurately infer the public hypothesis while protecting both inference and data privacy.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.relation2017-T1-001-059 (RG20/17)en_US
dc.relation.ispartofIEEE Transactions on Information Forensics and Securityen_US
dc.rights© 2019 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleOn the relationship between inference and data privacy in decentralized IoT networksen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.subject.keywordsInference Privacyen_US
dc.subject.keywordsData Privacyen_US
dc.description.acknowledgementThis work was supported by the Singapore Ministry of Education Academic Research Fund under Tier 1 Grant 2017-T1-001-059 (RG20/17) and Tier 2 Grant MOE2018-T2-2-019. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Mauro Conti.en_US
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