A General Proximity Privacy Principle

This work presents a systematic study of the problem of protecting general proximity privacy, with findings applicable to most existing data models. Our contributions are multi-folded: we highlighted and formulated proximity privacy breaches in a data-model-neutral manner; we proposed a new privacy principle (epsiv,delta)^k-dissimilarity, with theoretically guaranteed protection against linking attacks in terms of both exact and proximate QI-SA associations; we provided a theoretical analysis regarding the satisfiability of (epsiv,delta)^k -dissimilarity, and pointed to promising solutions to fulfilling this principle.