Statistical appliance inference in the smart grid by machine learning

Bilgin, Zeki, Tomur, Emrah, Ersoy, Mehmet Akif and Soykan, Elif Ustundag (2019) Statistical appliance inference in the smart grid by machine learning. In: 2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019. 2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019 . Institute of Electrical and Electronics Engineers Inc., TUR. ISBN 9781538693582

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Abstract

Smart Grid has been attracting more interest than ever thanks to emergence of enabling technologies such as 5G and IoT. Yet, there are some long-standing privacy concerns about revealing habits and lifestyles of people from fine-grained power consumption data collected through smart meters. In this context, the contribution of this work is twofold: First, we empirically demonstrate how appliance-level fine-grained power consumption data can reveal households' routines simply using probability density estimations derived from consumption data without requiring any complex analysis. Second, we point out that appliance types can be identified in a targeted house using circuit-level consumption data of other houses. We show how machine learning can be used maliciously to realize this threat in an automatic manner and achieve high success rate even with limited amount of training data on the public REDD dataset. In addition, we provide discussions on possible countermeasures against the threats examined in this study.

Item Type: Book Section
Additional Information: Funding Information: ACKNOWLEDGEMENT This work was funded by The Scientific and Technological Research Council of Turkey, under 1515 Frontier RD Laboratories Support Program with project no: 5169902. Publisher Copyright: © 2019 IEEE.
Uncontrolled Keywords: appliance identification and inference,machine learning,privacy,smart grid,statistical load signature,safety, risk, reliability and quality,computer networks and communications,sdg 7 - affordable and clean energy ,/dk/atira/pure/subjectarea/asjc/2200/2213
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 18 Aug 2022 12:30
Last Modified: 01 Oct 2022 08:32
URI: https://ueaeprints.uea.ac.uk/id/eprint/87432
DOI: 10.1109/PIMRCW.2019.8880846

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