Analyzing Continuous ks-Anonymization for Smart Meter Data
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