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dc.contributor.authorLombardi, Alex
dc.contributor.authorVaikuntanathan, Vinod
dc.contributor.authorWichs, Daniel
dc.date.accessioned2022-09-06T19:01:51Z
dc.date.available2021-11-03T14:46:12Z
dc.date.available2022-09-06T19:01:51Z
dc.date.issued2020-02
dc.identifier.urihttps://hdl.handle.net/1721.1/137207.2
dc.description.abstract© 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].en_US
dc.language.isoen
dc.publisherSpringer International Publishingen_US
dc.relation.isversionof10.1007/978-3-030-45727-3_21en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther repositoryen_US
dc.titleStatistical zapr arguments from bilinear mapsen_US
dc.typeArticleen_US
dc.identifier.citation2020. "Statistical zapr arguments from bilinear maps." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12107 LNCS.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-04-05T13:19:19Z
dspace.orderedauthorsLombardi, A; Vaikuntanathan, V; Wichs, Den_US
dspace.date.submission2021-04-05T13:19:21Z
mit.journal.volume12107 LNCSen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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