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Privacy issues in s...
Privacy issues in smart grid data : from energy disaggregation to disclosure risk
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- Adewole, Kayode Sakariyah (författare)
- Umeå universitet,Institutionen för datavetenskap,Department of Computer Science, University of Ilorin, Ilorin, Nigeria
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- Torra, Vicenç (författare)
- Umeå universitet,Institutionen för datavetenskap
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(creator_code:org_t)
- 2022-07-29
- 2022
- Engelska.
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Ingår i: Database and expert systems applications. - Cham : Springer. - 9783031124228 - 9783031124235 ; , s. 71-84
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The advancement in artificial intelligence (AI) techniques has given rise to the success rate recorded in the field of Non-Intrusive Load Monitoring (NILM). The development of robust AI and machine learning algorithms based on deep learning architecture has enabled accurate extraction of individual appliance load signature from aggregated energy data. However, the success rate of NILM algorithm in disaggregating individual appliance load signature in smart grid data violates the privacy of the individual household lifestyle. This paper investigates the performance of Sequence-to-Sequence (Seq2Seq) deep learning NILM algorithm in predicting the load signature of appliances. Furthermore, we define a new notion of disclosure risk to understand the risk associated with individual appliances in aggregated signals. Two publicly available energy disaggregation datasets have been considered. We simulate three inference attack scenarios to better ascertain the risk of publishing raw energy data. In addition, we investigate three activation extraction methods for appliance event detection. The results show that the disclosure risk associated with releasing smart grid data in their original form is on the high side. Therefore, future privacy protection mechanisms should devise efficient methods to reduce this risk.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Data privacy
- Disclosure risk
- Energy disaggregation
- Non-intrusive load monitoring
- Smart grid data
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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