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Analyzing Continuous ks-Anonymization for Smart Meter Data

Brunn, Carolin; Nuñez von Voigt, Saskia; Tschorsch, Florian

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 ks-anonymization with CASTLE for data streams generated by smart meters. We compare CASTLE’s continuous, piecewise ks-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 ks-anonymization of smart meter data is reasonable and ensures privacy while having comparably low utility loss.
Published in: ESORICS 2023 International Workshops, Springer Nature