CryptoDB
PIR with Client-Side Preprocessing: Information-Theoretic Constructions and Lower Bounds
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| Conference: | CRYPTO 2024 |
| Abstract: | It is well-known that classical Private Information Retrieval (PIR) schemes without preprocessing must suffer from linear server com- putation per query, and moreover, any classical single-server PIR with sublinear bandwidth must rely on “public-key operations”. Several re- cent works showed that these barriers pertaining to classical PIR can be overcome by introducing a preprocessing phase where each client down- loads a hint that helps it makes queries subsequently. Notably, the Piano PIR scheme (and subsequent improvements) showed that with such a client-specific preprocessing, not only can we have PIR with sublinear computation and bandwidth per query, somewhat surprisingly, we can also get it using only symmetric-key operations (i.e., one-way functions). In this paper, we take the question of minimizing cryptographic assump- tions to an extreme. Specifically, we are the first to explore the landscape of information theoretic single-server preprocessing PIR. We make con- tributions on both the upper- and lower-bounds fronts. First, we show new information-theoretic constructions with non-trivial performance bounds. Second, we prove a (nearly) tight lower bound on the client- space and bandwidth tradeoff. Moreover, we also prove that natural ap- proaches towards constructing preprocessing PIR with better-than-Piano client-space/bandwidth tradeoff would imply a hard SZK problem which cannot be constructed in a black-box fashion from one-way functions or collision-resistant hashing. This shows that Piano achieves (nearly) opti- mal client space and bandwidth tradeoff subject to using only symmetric- key operations. The techniques for proving our new upper- and lower- bounds can also be of independent interest. |
BibTeX
@inproceedings{crypto-2024-34339,
title={PIR with Client-Side Preprocessing: Information-Theoretic Constructions and Lower Bounds},
publisher={Springer-Verlag},
doi={10.1007/978-3-031-68400-5_5},
author={Yuval Ishai and Elaine Shi and Daniel Wichs},
year=2024
}