International Association for Cryptologic Research

International Association
for Cryptologic Research


Hardness of Non-interactive Differential Privacy from One-Way Functions

Lucas Kowalczyk
Tal Malkin
Jonathan Ullman
Daniel Wichs
DOI: 10.1007/978-3-319-96884-1_15 (login may be required)
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Presentation: Slides
Conference: CRYPTO 2018
Abstract: A central challenge in differential privacy is to design computationally efficient non-interactive algorithms that can answer large numbers of statistical queries on a sensitive dataset. That is, we would like to design a differentially private algorithm that takes a dataset $$D \in X^n$$D∈Xn consisting of some small number of elements n from some large data universe X, and efficiently outputs a summary that allows a user to efficiently obtain an answer to any query in some large family Q.Ignoring computational constraints, this problem can be solved even when X and Q are exponentially large and n is just a small polynomial; however, all algorithms with remotely similar guarantees run in exponential time. There have been several results showing that, under the strong assumption of indistinguishability obfuscation, no efficient differentially private algorithm exists when X and Q can be exponentially large. However, there are no strong separations between information-theoretic and computationally efficient differentially private algorithms under any standard complexity assumption.In this work we show that, if one-way functions exist, there is no general purpose differentially private algorithm that works when X and Q are exponentially large, and n is an arbitrary polynomial. In fact, we show that this result holds even if X is just subexponentially large (assuming only polynomially-hard one-way functions). This result solves an open problem posed by Vadhan in his recent survey [52].
Video from CRYPTO 2018
  title={Hardness of Non-interactive Differential Privacy from One-Way Functions},
  booktitle={Advances in Cryptology – CRYPTO 2018},
  series={Lecture Notes in Computer Science},
  author={Lucas Kowalczyk and Tal Malkin and Jonathan Ullman and Daniel Wichs},