The influence of dataset characteristics on privacy preserving methods in the advanced metering infrastructure

In this paper we study the effect of Advanced Metering Infrastructure (AMI) dataset characteristics on privacy preserving solutions previously proposed in the literature. We focus on common characteristics (data granularity, retention time and use of pseudonyms) and we study their effect on two privacy violations: de-anonymization and de-pseudonymization. In order to better understand their effect, we study the capabilities of the adversary through its modeling and description by a probabilistic framework. We perform evaluations on a large dataset collected from a real AMI environment. Our results show that simple changes in the data collection procedure can help mitigate the outcome of these privacy violations.