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Statistical Zero-Knowledge Arguments for NP from Any One-Way Function
Berkeley, California October 21-October 24
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FOCS.2006.7147th Annual IEEE Symposium on Foundat ...
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Minh-Huyen Nguyen, Harvard University, USA
Shien Jin Ong, Harvard University, USA
Salil Vadhan, Harvard University, USA
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 the verifier to accept a false assertion except with negligible probability. This resolves an open question posed by Naor, Ostrovsky, Venkatesan, and Yung (CRYPTO ?92, J. Cryptology ?98).

Departing from previous works on this problem, we do not construct standard statistically hiding commitments from any one-way function. Instead, we construct a relaxed variant of commitment schemes called "1-out-of-2-binding commitments," recently introduced by Nguyen and Vadhan (STOC ?06).

Citation:
Minh-Huyen Nguyen, Shien Jin Ong, Salil Vadhan, "Statistical Zero-Knowledge Arguments for NP from Any One-Way Function," focs, pp.3-14, 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06), 2006
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