Abstract. The definition of differential privacy has recently emerged as a leading standard of privacy guarantees for algorithms on statistical databases. We offer several relaxations of the definition which require privacy guarantees to hold only against efficient-i.e., computationallybounded-adversaries. We establish various relationships among these notions, and in doing so, we observe their close connection with the theory of pseudodense sets by Reingold et al. [1] . We extend the dense model theorem of Reingold et al. to demonstrate equivalence between two definitions (indistinguishability-and simulatability-based) of computational differential privacy. Our computational analogues of differential privacy seem to allow for more accurate constructions than the standard information-theoretic analogues. In particular, in the context of private approximation of the distance between two vectors, we present a differentially-private protocol for computing the approximation, and contrast it with a substantially more accurate protocol that is only computationally differentially private.
Following our work, computational differential privacy was considered in REF ; they present several definitions of computational differential privacy, study the relationships between these definitions, and construct efficient 2-party computational differentially private protocols for approximating the distance between two vectors.
Computational Differential Privacy
CRYPTO
Computer Science