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dc.contributor.authorMoradi, Ashkan
dc.contributor.authorDasanadoddi Venkategowda, Naveen Kumar
dc.contributor.authorTalebi, Sayedpouria
dc.contributor.authorWerner, Stefan
dc.date.accessioned2022-12-29T13:26:35Z
dc.date.available2022-12-29T13:26:35Z
dc.date.created2022-07-02T13:56:28Z
dc.date.issued2022
dc.identifier.citationIEEE Transactions on Signal Processing. 2022, 70 3074-3089.en_US
dc.identifier.issn1053-587X
dc.identifier.urihttps://hdl.handle.net/11250/3039905
dc.descriptionDistributed Kalman filtering techniques enable agents of a multiagent network to enhance their ability to track a system and learn from local cooperation with neighbors. Enabling this cooperation, however, requires agents to share information, which raises the question of privacy. This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) that protects local information of agents by restricting and obfuscating the information exchanged. The derived PP-DKF embeds two state-of-the-art average consensus techniques that guarantee agent privacy. The resulting PP-DKF utilizes noise injection-based and decomposition-based privacy-preserving techniques to implement a robust distributed Kalman filtering solution against perturbation. We characterize the performance and convergence of the proposed PP-DKF and demonstrate its robustness against the injected noise variance. We also assess the privacy-preserving properties of the proposed algorithm for two types of adversaries, namely, an external eavesdropper and an honest-but-curious (HBC) agent, by providing bounds on the privacy leakage for both adversaries. Finally, several simulation examples illustrate that the proposed PP-DKF achieves better performance and higher privacy levels than the distributed Kalman filtering solutions employing contemporary privacy-preserving techniques.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titlePrivacy-preserving distributed Kalman filteringen_US
dc.title.alternativePrivacy-preserving distributed Kalman filteringen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2022 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 worksen_US
dc.source.pagenumber3074-3089en_US
dc.source.volume70en_US
dc.source.journalIEEE Transactions on Signal Processingen_US
dc.identifier.doi10.1109/TSP.2022.3182590
dc.identifier.cristin2036859
dc.relation.projectNorges forskningsråd: 274717en_US
dc.relation.projectNorges forskningsråd: 300102en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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