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Electronic Colloquium on Computational Complexity
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REPORTS > AUTHORS > SHIEN JIN ONG:
All reports by Author Shien Jin Ong:

TR06-139 | 14th November 2006
Shien Jin Ong, Salil Vadhan

Zero Knowledge and Soundness are Symmetric

Revisions: 1
We give a complexity-theoretic characterization of the class of problems in NP having zero-knowledge argument systems that is symmetric in its treatment of the zero knowledge and the soundness conditions. From this, we deduce that the class of problems in NP intersect coNP having zero-knowledge arguments is closed under complement. ... more >>>

TR06-075 | 19th June 2006
Minh-Huyen Nguyen, Shien Jin Ong, Salil Vadhan

Statistical Zero-Knowledge Arguments for NP from Any One-Way Function

We show that every language in NP has a *statistical* zero-knowledge argument system under the (minimal) complexity assumption that one-way functions exist. In such protocols, even a computationally unbounded verifier cannot learn anything other than the fact that the assertion being proven is true, whereas a polynomial-time prover cannot convince ... more >>>

TR05-114 | 9th October 2005
Boaz Barak, Shien Jin Ong, Salil Vadhan

Derandomization in Cryptography

We give two applications of Nisan--Wigderson-type ("non-cryptographic") pseudorandom generators in cryptography. Specifically, assuming the existence of an appropriate NW-type generator, we construct: A one-message witness-indistinguishable proof system for every language in NP, based on any trapdoor permutation. This proof system does not assume a shared random string or any setup ... more >>>

TR05-093 | 24th August 2005
Daniele Micciancio, Shien Jin Ong, Amit Sahai, Salil Vadhan

Concurrent Zero Knowledge without Complexity Assumptions

We provide unconditional constructions of concurrent statistical zero-knowledge proofs for a variety of non-trivial problems (not known to have probabilistic polynomial-time algorithms). The problems include Graph Isomorphism, Graph Nonisomorphism, Quadratic Residuosity, Quadratic Nonresiduosity, a restricted version of Statistical Difference, and approximate versions of the (coNP forms of the) Shortest Vector ... more >>>



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