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dc.contributor.authorSong, Yangen_US
dc.contributor.authorWang, Chong Xiaoen_US
dc.contributor.authorTay, Wee Pengen_US
dc.identifier.citationSong, Y., Wang, C. X. & Tay, W. P. (2020). Compressive privacy for a linear dynamical system. IEEE Transactions On Information Forensics and Security, 15, 895-910.
dc.description.abstractWe consider a linear dynamical system in which the state vector consists of both public and private states. One or more sensors make measurements of the state vector and sends information to a fusion center, which performs the final state estimation. To achieve an optimal tradeoff between the utility of estimating the public states and protection of the private states, the measurements at each time step are linearly compressed into a lower dimensional space. Under the centralized setting where all measurements are collected by a single sensor, we propose an optimization problem and an algorithm to find the best compression matrix. Under the decentralized setting where measurements are made separately at multiple sensors, each sensor optimizes its own local compression matrix. We propose methods to separate the overall optimization problem into multiple sub-problems that can be solved locally at each sensor. We consider the cases where there is no message exchange between the sensors; and where each sensor takes turns to transmit messages to the other sensors. Simulations and empirical experiments demonstrate the efficiency of our proposed approach in allowing the fusion center to estimate the public states with good accuracy while preventing it from estimating the private states accurately.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofIEEE Transactions on Information Forensics and Securityen_US
dc.rights© 2019 IEEEen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleCompressive privacy for a linear dynamical systemen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.subject.keywordsInference Privacyen_US
dc.subject.keywordsCompressive Privacyen_US
dc.description.acknowledgementThis work was supported in part by the ST Engineering NTU Corporate Lab through the NRF Corporate Lab@University Scheme Project Reference C-RP10B, and in part by the Singapore Ministry of Education Academic Research Fund Tier 2 Grant MOE2018-T2-2-019. This article was presented in part at the IEEE International Conference on Acoustics, Speech, and Signal Processing, Calgary, Canada, 2018. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Marina Blanton.en_US
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