Advances in Cryptology - CRYPTO 2009

Volume 5677 of the series Lecture Notes in Computer Science pp 126-142

Computational Differential Privacy

  • Ilya MironovAffiliated withMicrosoft Research
  • , Omkant PandeyAffiliated withUniversity of California
  • , Omer ReingoldAffiliated withDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science
  • , Salil VadhanAffiliated withSchool of Engineering and Applied Sciences and Center for Research on Computation and Society, Harvard University


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., computationally-bounded—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.