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Maximizing Privacy in MIMO Cyber-Physical Systems Using the Chapman-Robbins Bound
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-6558-3807
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.ORCID iD: 0000-0002-2300-2581
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-1835-2963
2020 (English)In: Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 6272-6277Conference paper, Published paper (Refereed)
Abstract [en]

Privacy breaches of cyber-physical systems could expose vulnerabilities to an adversary. Here, privacy leaks of step inputs to linear time-invariant systems are mitigated through additive Gaussian noise. Fundamental lower bounds on the privacy are derived, which are based on the variance of any estimator that seeks to recreate the input. Fully private inputs are investigated and related to transmission zeros. Thereafter, a method to increase the privacy of optimal step inputs is presented and a privacy-utility trade-off bound is derived. Finally, these results are verified on data from the KTH Live-In Lab Testbed, showing good correspondence with theoretical results. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2020. p. 6272-6277
Keywords [en]
Cyber Physical System, Economic and social effects, Gaussian noise (electronic), Invariance, Linear systems, Time varying control systems, Additive Gaussian noise, Linear time invariant systems, Lower bounds, Privacy breaches, Trade off, Transmission zeros, Embedded systems
National Category
Control Engineering Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-301201DOI: 10.1109/CDC42340.2020.9303912ISI: 000717663405003Scopus ID: 2-s2.0-85099876230OAI: oai:DiVA.org:kth-301201DiVA, id: diva2:1591760
Conference
59th IEEE Conference on Decision and Control, CDC 2020, 14 December 2020 through 18 December 2020
Funder
Swedish Foundation for Strategic Research
Note

QC 20230307

Available from: 2021-09-07 Created: 2021-09-07 Last updated: 2023-03-07Bibliographically approved

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Alisic, RijadMolinari, MarcoSandberg, Henrik

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CiteExportLink to record
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  • apa
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Output format
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