Statistical zapr arguments from bilinear maps

© International Association for Cryptologic Research 2020. Dwork and Naor (FOCS '00) defined ZAPs as 2-message witness-indistinguishable proofs that are public-coin. We relax this to ZAPs with private randomness (ZAPRs), where the verifier can use private coins to sample the first message (inde...

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Language:English
Published: Springer International Publishing, 2021-11-03T14:46:12Z.
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245 0 0 |a Statistical zapr arguments from bilinear maps 
260 |b Springer International Publishing,   |c 2021-11-03T14:46:12Z. 
856 |z Get fulltext  |u https://hdl.handle.net/1721.1/137207 
520 |a © International Association for Cryptologic Research 2020. Dwork and Naor (FOCS '00) defined ZAPs as 2-message witness-indistinguishable proofs that are public-coin. We relax this to ZAPs with private randomness (ZAPRs), where the verifier can use private coins to sample the first message (independently of the statement being proved), but the proof must remain publicly verifiable given only the protocol transcript. In particular, ZAPRs are reusable, meaning that the first message can be reused for multiple proofs without compromising security. Known constructions of ZAPs from trapdoor permutations or bilinear maps are only computationally WI (and statistically sound). Two recent results of Badrinarayanan-Fernando-Jain-Khurana-Sahai and Goyal-Jain-Jin-Malavolta [EUROCRYPT '20] construct the first statistical ZAP arguments, which are statistically WI (and computationally sound), from the quasi-polynomial LWE assumption. Here, we construct statistical ZAPR arguments from the quasi-polynomial decision-linear (DLIN) assumption on groups with a bilinear map. Our construction relies on a combination of several tools, including the Groth-Ostrovsky-Sahai NIZK and NIWI [EUROCRYPT '06, CRYPTO '06, JACM '12], "sometimes-binding statistically hiding commitments" [Kalai-Khurana-Sahai, EUROCRYPT '18] and the "MPC-in-the-head" technique [Ishai-Kushilevitz-Ostrovsky-Sahai, STOC '07]. 
546 |a en 
655 7 |a Article 
773 |t 10.1007/978-3-030-45727-3_21 
773 |t Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)