CryptoDB
Time-Lock Puzzles from Lattices
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| Conference: | CRYPTO 2024 |
| Abstract: | Time-lock puzzles (TLP) are a cryptographic tool that allow one to encrypt a message into the future, for a predetermined amount of time T . At present, we have only two constructions with provable security: One based on the repeated squaring assumption and the other based on indistinguishability obfuscation (iO). Basing TLP on any other assumption is a long-standing question, further motivated by the fact that know constructions are broken by quantum algorithms. In this work, we propose a new approach to construct time-lock puzzles based on lattices, and therefore with plausible post-quantum security. We obtain the following main results: • In the preprocessing model, where a one-time public-coin preprocessing is allowed, we obtain a time-lock puzzle with encryption time log(T ). • In the plain model, where the encrypter does all the computation, we obtain a time-lock puzzle with encryption time √T . Both constructions assume the existence of any sequential function f , and the hardness of the circular small-secret learning with errors (LWE) problem. At the heart of our results is a new construction of succinct randomized encodings (SRE) for T-folded repeated circuits, where the complexity of the encoding is √T . This is the first construction of SRE where the overall complexity of the encoding algorithm is sublinear in the runtime T , and which is not based on iO. Using our SRE we directly obtain the first non- interactive RAM delegation scheme with sublinear complexity (in the number of steps T ), again without iO. Finally, we also propose a new heuristic construction of SREs, and consequently of TLPs, with fully-efficient encoding complexity log(T ). Our scheme is inspired by iO techniques, but carefully sidesteps the regime of zeroizing attacks that plague lattice-based iO candidates. |
BibTeX
@inproceedings{crypto-2024-34246,
title={Time-Lock Puzzles from Lattices},
publisher={Springer-Verlag},
doi={10.1007/978-3-031-68382-4_13},
author={Shweta Agrawal and Giulio Malavolta and Tianwei Zhang},
year=2024
}