Publikationer från Malmö universitet
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  • 1.
    Adewole, Kayode S.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Umeå Univ, Dept Comp Sci, Umeå, Sweden.;Univ Ilorin, Dept Comp Sci, Ilorin, Nigeria..
    Torra, Vicenc
    Umeå Univ, Dept Comp Sci, Umeå, Sweden..
    Privacy Protection of Synthetic Smart Grid Data Simulated via Generative Adversarial Networks2023Inngår i: Proceedings of the 20th international conference on security and cryptography, secrypt 2023 / [ed] DiVimercati, SD; Samarati, P, SciTePress, 2023, s. 279-286Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The development in smart meter technology has made grid operations more efficient based on fine-grained electricity usage data generated at different levels of time granularity. Consequently, machine learning algorithms have benefited from these data to produce useful models for important grid operations. Although machine learning algorithms need historical data to improve predictive performance, these data are not readily available for public utilization due to privacy issues. The existing smart grid data simulation frameworks generate grid data with implicit privacy concerns since the data are simulated from a few real energy consumptions that are publicly available. This paper addresses two issues in smart grid. First, it assesses the level of privacy violation with the individual household appliances based on synthetic household aggregate loads consumption. Second, based on the findings, it proposes two privacy-preserving mechanisms to reduce this risk. Three inference attacks are simulated and the results obtained confirm the efficacy of the proposed privacy-preserving mechanisms.

    Fulltekst (pdf)
    fulltext
  • 2.
    Adewole, Kayode Sakariyah
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Department of Computer Science, University of Ilorin, Ilorin, Nigeria.
    Torra, Vicenç
    Department of Computing Science, Umeå University, Sweden.
    Energy disaggregation risk resilience through microaggregation and discrete Fourier transform2024Inngår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 662, artikkel-id 120211Artikkel i tidsskrift (Fagfellevurdert)
    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.

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