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On Worst-Case to Average-Case Reductions for NP Problems
Cambridge, Massachusettes October 11-October 14
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SFCS.2003.123820544th Annual IEEE Symposium on Foundat ...
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Andrej Bogdanov, University of California at Berkeley
Luca Trevisan, University of California at Berkeley

We show that if an NP-complete problem has a non-adaptive self-corrector with respect to a samplable distribution then coNP is contained in AM/poly and the polynomial hierarchy collapses to the third level. Feigenbaum and Fortnow show the same conclusion under the stronger assumption that an NP-complete problem has a non-adaptive random self-reduction.

Our result shows it is impossible (using non-adaptive reductions) to base the average-case hardness of a problem in NP or the security of a one-way function on the worst-case complexity of an NP-complete problem (unless the polynomial hierarchy collapses).

Citation:
Andrej Bogdanov, Luca Trevisan, "On Worst-Case to Average-Case Reductions for NP Problems," focs, pp.308, 44th Annual IEEE Symposium on Foundations of Computer Science (FOCS'03), 2003
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