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dc.contributor.authorBerman, Itay
dc.contributor.authorRothblum, Ron D.
dc.contributor.authorVaikuntananthan, Vinod
dc.date.accessioned2021-11-08T20:18:12Z
dc.date.available2021-11-08T20:18:12Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/1721.1/137815
dc.description.abstract© Itay Berman, Ron D. Rothblum and Vinod Vaikuntanathan. Interactive proofs of proximity (IPPs) are interactive proofs in which the verifier runs in time sub-linear in the input length. Since the verifier cannot even read the entire input, following the property testing literature, we only require that the verifier reject inputs that are far from the language (and, as usual, accept inputs that are in the language). In this work, we initiate the study of zero-knowledge proofs of proximity (ZKPP). A ZKPP convinces a sub-linear time verifier that the input is close to the language (similarly to an IPP) while simultaneously guaranteeing a natural zero-knowledge property. Specifically, the verifier learns nothing beyond (1) the fact that the input is in the language, and (2) what it could additionally infer by reading a few bits of the input. Our main focus is the setting of statistical zero-knowledge where we show that the following hold unconditionally (where N denotes the input length): Statistical ZKPPs can be sub-exponentially more efficient than property testers (or even non-interactive IPPs): We show a natural property which has a statistical ZKPP with a polylog(N) time verifier, but requires (N) queries (and hence also runtime) for every property tester. Statistical ZKPPs can be sub-exponentially less efficient than IPPs: We show a property which has an IPP with a polylog(N) time verifier, but cannot have a statistical ZKPP with even an No(1) time verifier. Statistical ZKPPs for some graph-based properties such as promise versions of expansion and bipartiteness, in the bounded degree graph model, with polylog(N) time verifiers exist. Lastly, we also consider the computational setting where we show that: Assuming the existence of one-way functions, every language computable either in (logspace uniform) NC or in SC, has a computational ZKPP with a (roughly) N time verifier. Assuming the existence of collision-resistant hash functions, every language in NP has a statistical zero-knowledge argument of proximity with a polylog(N) time verifier.en_US
dc.language.isoen
dc.relation.isversionof10.4230/LIPIcs.ITCS.2018.19en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceDROPSen_US
dc.titleZero-Knowledge Proofs of Proximityen_US
dc.typeArticleen_US
dc.identifier.citationBerman, Itay, Rothblum, Ron D. and Vaikuntananthan, Vinod. 2018. "Zero-Knowledge Proofs of Proximity."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-07-09T17:01:20Z
dspace.date.submission2019-07-09T17:01:21Z
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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