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Ensuring privacy of occupancy changes in smart buildings
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.ORCID iD: 0000-0001-6558-3807
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-2300-2581
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-4095-7320
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration. 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: CCTA 2020 - 4th IEEE Conference on Control Technology and Applications, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 871-876Conference paper, Published paper (Refereed)
Abstract [en]

Smart building management systems rely on sensors to optimize the operation of buildings. If an unauthorized user gains access to these sensors, a privacy leak may occur. This paper considers such a potential leak of privacy in a smart residential building, and how it may be mitigated by corrupting the measurements with additive Gaussian noise. This corruption is done in order to hide when the occupancy changes in an apartment. A lower bound on the variance of any estimator that estimates the change time is derived. The bound is then used to analyze how different model parameters affect the variance. It is shown that the signal to noise ratio and the system dynamics are the main factors that affect the bound. These results are then verified on a simulator of 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. 871-876
Keywords [en]
Intelligent buildings, Signal to noise ratio, Additive Gaussian noise, Building management system, Change time, Lower bounds, Model parameters, Residential building, System Dynamics, Unauthorized users, Gaussian noise (electronic)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-290350DOI: 10.1109/CCTA41146.2020.9206317ISI: 000668042200132Scopus ID: 2-s2.0-85094102905OAI: oai:DiVA.org:kth-290350DiVA, id: diva2:1528727
Conference
4th IEEE Conference on Control Technology and Applications, CCTA 2020, 24 August 2020 through 26 August 2020
Note

QC 20230307

Available from: 2021-02-16 Created: 2021-02-16 Last updated: 2023-03-07Bibliographically approved

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Alisic, RijadMolinari, MarcoPare, Philip E.Sandberg, Henrik

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Decision and Control Systems (Automatic Control)Applied Thermodynamics and Refrigeration
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