Event-set differential privacy for fine-grained data privacy protection.

• We propose a fine-grained privacy model called α -event-set differential privacy which can achieve the desired granularity of privacy protection while allowing good data utility. • We present the definition, properties, and baseline mechanisms of α -event-set differential privacy. • We implement and evaluate α -event-set differential privacy on different statistical and analyzing applications, including mean estimation, histogram estimation, and machine learning. Privacy-preserving data statistics and analysis has become an urgent problem nowadays. Differential privacy (DP), as a rigorous privacy paradigm, has been widely adopted in various fields. However, in the context of large-scale mobile applications where each user has multiple records, both user-level DP and record-level DP cannot achieve a good compromise between stringent privacy and high data utility. A more satisfying privacy paradigm with desired granularity becomes very necessary. To this end, this paper proposes a fine-grained privacy paradigm called α -event-set differential privacy, which prevents adversaries from inferring any one of α event-sets owned by the user in data statistics and analysis. We theoretically introduce the definition, properties, and baseline mechanisms of α -event-set DP. Besides, we implement and evaluate α -event-set DP on mean estimation, histogram estimation, and machine learning applications, respectively. The experimental results have shown that α -event-set DP is able to achieve a fine-grained granularity of privacy protection while allowing high data utility. [ABSTRACT FROM AUTHOR]