Sheikh, Nazim Uddin, Lu, Zhigang, Asghar, Hassan Jameel, and Kaafar, Mohamed (2021) Trace Recovery: Inferring Fine-grained Trace of Energy Data from Aggregates. In: Proceedings of the 18th International Conference on Security and Cryptography (1) pp. 283-274. From: SECRYPT: 18th International Conference on Security and Cryptography, 6-8 July 2021, Online.

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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 ti me-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.

Item ID:	82432
Item Type:	Conference Item (Research - E1)
ISBN:	978-989-758-524-1
Copyright Information:	© 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. CC BY-NC-ND 4.0.
Date Deposited:	01 May 2024 01:53
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460402 Data and information privacy @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220405 Cybersecurity @ 100%
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