Abstract
Data anonymization is crucial to allow the widespread adoption of some technologies, such as smart meters. However, anonymization techniques should be evaluated in the context of a dataset to make meaningful statements about their eligibility for a particular use case. In this paper, we therefore analyze the suitability of continuous \(k_s\)-anonymization with CASTLE for data streams generated by smart meters. We compare CASTLE ’s continuous, piecewise \(k_s\)-anonymization with a global process in which all data is known at once, based on metrics like information loss and properties of the sensitive attribute. Our results suggest that continuous \(k_s\)-anonymization of smart meter data is reasonable and ensures privacy while having comparably low utility loss.
Supported by the Federal Ministry of Education and Research of Germany (Project 16KISA034).
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Notes
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The magnitude of consumption values suggests that the values are given in Watt instead of kW as noted in the description of the dataset.
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We have reached out to the developers to discuss the bugs/changes.
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Brunn, C., von Voigt, S.N., Tschorsch, F. (2024). Analyzing Continuous K\(_{s}\)-Anonymization for Smart Meter Data. In: Katsikas, S., et al. Computer Security. ESORICS 2023 International Workshops. ESORICS 2023. Lecture Notes in Computer Science, vol 14398. Springer, Cham. https://doi.org/10.1007/978-3-031-54204-6_16
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