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Energy disaggregation risk resilience through microaggregation and discrete Fourier transform
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Department of Computer Science, University of Ilorin, Ilorin, Nigeria.ORCID iD: 0000-0002-0155-7949
Department of Computing Science, Umeå University, Sweden.
2024 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 662, article id 120211Article in journal (Refereed) Published
Abstract [en]

Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 662, article id 120211
Keywords [en]
Smart meters, Smart grid, Disclosure risk, Non-intrusive load monitoring, Data privacy, Microaggregation, Discrete Fourier transform
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mau:diva-66922DOI: 10.1016/j.ins.2024.120211ISI: 001178010000001Scopus ID: 2-s2.0-85183847925OAI: oai:DiVA.org:mau-66922DiVA, id: diva2:1854563
Available from: 2024-04-26 Created: 2024-04-26 Last updated: 2024-04-26Bibliographically approved

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Adewole, Kayode Sakariyah

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