We demonstrate that, ignoring computational constraints, it is possible to release privacy-preserving databases that are useful for all queries over a discretized domain from any given concept class with polynomial VC-dimension. We show a new lower bound for releasing databases that are useful for halfspace queries over a continuous domain. Despite this, we give a privacy-preserving polynomial time algorithm that releases information useful for all halfspace queries, for a slightly relaxed definition of usefulness. Inspired by learning theory, we introduce a new notion of data privacy, which we call distributional privacy, and show that it is strictly stronger than the prevailing privacy notion, differential privacy.
Blum et al. REF demonstrate that it is possible to release synthetic private databases that are useful for all queries over a discretized domain from a concept class with polynomial Vapnik-Chervonenkis dimension.
A learning theory approach to non-interactive database privacy
STOC '08
Mathematics Computer Science