Strong Hardness of Privacy from Weak Traitor Tracing
Lucas Kowalczyk, Tal Malkin, Jonathan Ullman, Mark Zhandry

TL;DR
This paper establishes strong computational hardness results for answering statistical queries under differential privacy, linking these results to traitor-tracing schemes and demonstrating near-tight bounds.
Contribution
It proves new hardness results for differential privacy when either the query set or data universe is exponential, using traitor-tracing schemes with weak security as a basis.
Findings
Hardness results for exponential size query sets and data universes
Nearly tight bounds for differentially private query answering
Connection between traitor-tracing schemes and privacy hardness
Abstract
Despite much study, the computational complexity of differential privacy remains poorly understood. In this paper we consider the computational complexity of accurately answering a family of statistical queries over a data universe under differential privacy. A statistical query on a dataset asks "what fraction of the elements of satisfy a given predicate on ?" Dwork et al. (STOC'09) and Boneh and Zhandry (CRYPTO'14) showed that if both and are of polynomial size, then there is an efficient differentially private algorithm that accurately answers all the queries, and if both and are exponential size, then under a plausible assumption, no efficient algorithm exists. We show that, under the same assumption, if either the number of queries or the data universe is of exponential size, and the other has size at least , then…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Complexity and Algorithms in Graphs
