Analyzing and explaining privacy risks on time series data: ongoing work and challenges

Abstract

Currently, privacy risks assessment is mainly performed as audits conducted by data privacy analysts. In the TAILOR project, we promote a more systematic and automatic approach based on interpretable metrics and formal methods to evaluate privacy risks and to control the tension between data privacy and utility. In this paper, we focus on privacy risks raised by publishing time series datasets, and we survey the methods developed in TAILOR to analyze and quantify privacy risks depending on different publisher and attacker models.