Paper 2020/256

Statistical ZAPR Arguments from Bilinear Maps

Alex Lombardi, Vinod Vaikuntanathan, and Daniel Wichs

Abstract

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].

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
A minor revision of an IACR publication in EUROCRYPT 2020
Keywords
ZAPsStatistical Witness Indistinguishability
Contact author(s)
alexjl @ mit edu
History
2020-02-25: received
Short URL
https://ia.cr/2020/256
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2020/256,
      author = {Alex Lombardi and Vinod Vaikuntanathan and Daniel Wichs},
      title = {Statistical {ZAPR} Arguments from Bilinear Maps},
      howpublished = {Cryptology {ePrint} Archive, Paper 2020/256},
      year = {2020},
      url = {https://eprint.iacr.org/2020/256}
}
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