{"id":161885,"date":"2011-01-01T00:00:00","date_gmt":"2011-01-01T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/on-approximating-the-entropy-of-polynomial-mappings\/"},"modified":"2018-10-16T20:01:44","modified_gmt":"2018-10-17T03:01:44","slug":"on-approximating-the-entropy-of-polynomial-mappings","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/on-approximating-the-entropy-of-polynomial-mappings\/","title":{"rendered":"On Approximating the Entropy of Polynomial Mappings"},"content":{"rendered":"<p>We investigate the complexity of the following computational problem: Polynomial Entropy Approximation (PEA): Given a low-degree polynomial mapping p : Fn ! Fm, where F is a \fnite \feld, approximate the output entropy H(p(Un)), where Un is the uniform distribution on Fn and H may be any of several entropy measures. We show: \u000f Approximating the Shannon entropy of degree 3 polynomials p : Fn2 ! Fm2over F2 to within an additive constant\u00a0 (or even n:9) is complete for SZKPL, the class of problems having statistical zero-knowledge proofs where the honest veri\fer and its simulator are computable in logarithmic space. (SZKPL contains most of the natural problems known to be in the full class SZKP.) \u000f For prime \felds F 6= F2 and homogeneous quadratic polynomials p : Fn ! Fm, there is a probabilistic polynomial-time algorithm that distinguishes the case that p(Un) has entropy smaller than k from the case that p(Un) has min-entropy (or even Renyi entropy) greater than (2 + o(1))k. \u000f For degree d polynomials p : Fn2 ! Fm2 , there is a polynomial-time algorithm that distinguishes the case that p(Un) has max-entropy smaller than k (where the max-entropy of a random variable is the logarithm of its support size) from the case that p(Un) has max-entropy at least (1 + o(1)) \u0001 kd (for \fxed d and large k).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We investigate the complexity of the following computational problem: Polynomial Entropy Approximation (PEA): Given a low-degree polynomial mapping p : Fn ! Fm, where F is a \fnite \feld, approximate the output entropy H(p(Un)), where Un is the uniform distribution on Fn and H may be any of several entropy measures. We show: \u000f Approximating [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Tsinghua University Press","msr_publisher_other":"","msr_booktitle":"ICS","msr_chapter":"","msr_edition":"Proceedings of The Second Symposium on Innovations in Computer Science (ICS 2011), ICS","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of The Second Symposium on Innovations in Computer Science (ICS 2011), 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Dvir","user_id":0,"rest_url":false},{"type":"text","value":"Dan Gutfreund","user_id":0,"rest_url":false},{"type":"user_nicename","value":"guyroth","user_id":31933,"rest_url":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=guyroth"},{"type":"text","value":"Salil 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