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Massachusetts Institute of Technology

Information theoretic advances in zero-knowledge

Abstract

dc:description.abstract

Zero-knowledge proofs have an intimate relation to notions from information theory. In particular, the class of all problems possessing statistical zero-knowledge proofs (SZK) was shown to have complete problems characterized by the statistical distance (Sahai and Vadhan [JACM, 20031) and entropy difference (Goldreich and Vadhan [CCC, 19991) of a pair of efficiently samplable distributions. This characterization has been extremely beneficial in understanding the computational complexity of languages with zero-knowledge proofs and deriving new applications from such languages. In this thesis, we further study the relation between zero-knowledge proofs and information theory. We show the following results: 1. Two additional complete problems for SZK characterized by other information theoretic notions-triangular discrimination and Jensen-Shannon divergence.

Degree

thesis:*
Name thesis:degree_name
Doctoral
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Berman, Itay.
Advisor dc:contributor.advisor
  • Vinod Vaikuntanathan.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/122725
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/122725

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Berman, Itay.. Information theoretic advances in zero-knowledge. Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122725