TY - GEN
T1 - Trace recovery
T2 - 18th International Conference on Security and Cryptography, SECRYPT 2021
AU - Sheikh, Nazim Uddin
AU - Lu, Zhigang
AU - Asghar, Hassan Jameel
AU - Kaafar, Mohamed Ali
N1 - 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.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Aggregate Statistics
KW - Differential Privacy
KW - Energy Data Privacy
KW - Inference Attacks
KW - Smart Meter Privacy
UR - http://www.scopus.com/inward/record.url?scp=85111827428&partnerID=8YFLogxK
U2 - 10.5220/0010560302830294
DO - 10.5220/0010560302830294
M3 - Conference proceeding contribution
T3 - Proceedings of the International Conference on Security and Cryptography
SP - 283
EP - 294
BT - Proceedings of the 18th International Conference on Security and Cryptography, SECRYPT 2021
A2 - di Vimercati, Sabrina De Capitani
A2 - Samarati, Pierangela
PB - SciTePress
CY - Setúbal, Portugal
Y2 - 6 July 2021 through 8 July 2021
ER -