Logic, Probability, and Privacy: A Framework for Specifying Privacy Requirements

In this paper, we propose a probabilistic hybrid logic for the specification of data privacy requirements. The proposed logic is a combination of quantitative uncertainty logic and basic hybrid logic with a satisfaction operator. We show that it is expressive enough for the specification of many well-known data privacy requirements, such as k-anonymity, l-diversity and its precursor logical safety, t-closeness, and δ-disclosure privacy. The main contribution of the work is twofold. On one hand, the logic provides a common ground to express and compare existing privacy criteria. On the other hand, the uniform framework can meet the specification needs of combining new criteria as well as existing ones.