Trace recovery: inferring fine-grained trace of energy data from aggregates

Nazim Uddin Sheikh, Zhigang Lu, Hassan Jameel Asghar, Mohamed Ali Kaafar

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)
53 Downloads (Pure)

Abstract

Smart meter data is collected and shared with different stakeholders involved in a smart grid ecosystem. The fine-grained energy data is extremely useful for grid operations and maintenance, monitoring and for market segmentation purposes. However, sharing and releasing fine-grained energy data induces explicit violations of private information of consumers (Molina-Markham et al., 2010). Service providers do then share and release aggregated statistics to preserve the privacy of consumers with data aggregation aiming at reducing the risks of individual consumption traces being revealed. In this paper, we show that an adversary can reconstruct individual traces of energy data by exploiting consistency (similar consumption patterns over time) and distinctiveness (one household’s energy consumption pattern is significantly different from that of others) properties of individual consumption load patterns. We propose an unsupervised attack framework to recover hourly energy consumption time-series of individual users without any prior knowledge. We pose the problem of assigning aggregated energy consumption meter readings to individuals as an assignment problem and solve it by the Hungarian algorithm (Xu et al., 2017; Kuhn, 1955). Using two real-world datasets, our empirical evaluations show that an adversary is capable of recovering over 70% of households’ energy consumption patterns with over 90% accuracy.
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Security and Cryptography, SECRYPT 2021
EditorsSabrina De Capitani di Vimercati, Pierangela Samarati
Place of PublicationSetúbal, Portugal
PublisherSciTePress
Pages283-294
Number of pages12
ISBN (Electronic)9789897585241
DOIs
Publication statusPublished - 2021
Event18th International Conference on Security and Cryptography, SECRYPT 2021 - Virtual, Online
Duration: 6 Jul 20218 Jul 2021

Publication series

NameProceedings of the International Conference on Security and Cryptography
Number18
ISSN (Electronic)2184-7711

Conference

Conference18th International Conference on Security and Cryptography, SECRYPT 2021
CityVirtual, Online
Period6/07/218/07/21

Bibliographical note

Copyright the Publisher 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Aggregate Statistics
  • Differential Privacy
  • Energy Data Privacy
  • Inference Attacks
  • Smart Meter Privacy

Fingerprint

Dive into the research topics of 'Trace recovery: inferring fine-grained trace of energy data from aggregates'. Together they form a unique fingerprint.

Cite this