Trace Recovery: Inferring Fine-grained Trace of Energy Data from Aggregates

Nazim Sheikh, Zhigang Lu, Hassan Asghar, Hassan Asghar, Mohamed Kaafar

2021

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.

Paper Citation

in Harvard Style

Sheikh N., Lu Z., Asghar H. and Kaafar M. (2021). Trace Recovery: Inferring Fine-grained Trace of Energy Data from Aggregates. In Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-524-1, pages 283-294. DOI: 10.5220/0010560302830294

in Bibtex Style

@conference{secrypt21,
author={Nazim Sheikh and Zhigang Lu and Hassan Asghar and Mohamed Kaafar},
title={Trace Recovery: Inferring Fine-grained Trace of Energy Data from Aggregates},
booktitle={Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2021},
pages={283-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010560302830294},
isbn={978-989-758-524-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - Trace Recovery: Inferring Fine-grained Trace of Energy Data from Aggregates
SN - 978-989-758-524-1
AU - Sheikh N.
AU - Lu Z.
AU - Asghar H.
AU - Kaafar M.
PY - 2021
SP - 283
EP - 294
DO - 10.5220/0010560302830294