Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Private memoirs of a smart meter
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Privacy-friendly aggregation for the smart-grid
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Smart metering de-pseudonymization
Proceedings of the 27th Annual Computer Security Applications Conference
GridPriv: A Smart Metering Architecture Offering k-Anonymity
TRUSTCOM '12 Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
Evaluating electricity theft detectors in smart grid networks
RAID'12 Proceedings of the 15th international conference on Research in Attacks, Intrusions, and Defenses
Re-identification of Smart Meter data
Personal and Ubiquitous Computing
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The upgrade of the electricity network to the ``smart grid'' has been intensified in the last years. The new automated devices being deployed gather large quantities of data that offer promises of a more resilient grid but also raise privacy concerns among customers and energy distributors. In this paper, we focus on the energy consumption traces that smart meters generate and especially on the risk of being able to identify individual customers given a large dataset of these traces. This is a question raised in the related literature and an important privacy research topic. We present an overview of the current research regarding privacy in the Advanced Metering Infrastructure. We make a formalization of the problem of de-anonymization by matching low-frequency and high-frequency smart metering datasets and we also build a threat model related to this problem. Finally, we investigate the characteristics of these datasets in order to make them more resilient to the de-anonymization process. Our methodology can be used by electricity companies to better understand the properties of their smart metering datasets and the conditions under which such datasets can be released to third parties.