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  1. Transactions on Computation Theory (TOCT)
  2. ACM Transactions on Computation Theory (TOCT) : Volume 6
  3. Issue 3(Special issue on innovations in theoretical computer science 2012 - Part II), July 2014
  4. On the One-Way Function Candidate Proposed by Goldreich
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ACM Transactions on Computation Theory (TOCT) : Volume 9
ACM Transactions on Computation Theory (TOCT) : Volume 8
ACM Transactions on Computation Theory (TOCT) : Volume 7
ACM Transactions on Computation Theory (TOCT) : Volume 6
Issue 4, August 2014
Issue 3(Special issue on innovations in theoretical computer science 2012 - Part II), July 2014
Introduction to the Special Issue on Innovations in Theoretical Computer Science 2012 - Part II
Learning Hurdles for Sleeping Experts
Evolvability of Real Functions
(Leveled) Fully Homomorphic Encryption without Bootstrapping
On the One-Way Function Candidate Proposed by Goldreich
Issue 2, May 2014
Issue 1, March 2014
ACM Transactions on Computation Theory (TOCT) : Volume 5
ACM Transactions on Computation Theory (TOCT) : Volume 4
ACM Transactions on Computation Theory (TOCT) : Volume 3
ACM Transactions on Computation Theory (TOCT) : Volume 2
ACM Transactions on Computation Theory (TOCT) : Volume 1

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On the One-Way Function Candidate Proposed by Goldreich

Content Provider ACM Digital Library
Author Cook, James Miller, Rachel Trevisan, Luca Etesami, Omid
Copyright Year 2014
Abstract Goldreich [2000] proposed a candidate one-way function based on a bipartite graph of small right-degree $\textit{d},$ where the vertices on the left (resp. right) represent input (resp. output) bits of the function. Each output bit is computed by evaluating a fixed $\textit{d}-ary$ binary predicate on the input bits adjacent to that output bit. We study this function when the predicate is random or depends linearly on many of its input bits. We assume that the graph is a random balanced bipartite graph with right-degree $\textit{d}.$ Inverting this function as a one-way function by definition means finding an element in the preimage of output of this function for a random input. We bound the expected size of this preimage. Next, using the preceding bound, we prove that two restricted types of backtracking algorithms called $\textit{myopic}$ and $\textit{drunk}$ backtracking algorithms with high probability take exponential time to invert the function, even if we allow the algorithms to use DPLL elimination rules. (For drunk algorithms, a similar result was proved by Itsykson [2010].) We also ran a SAT solver on the satisfiability problem equivalent to the problem of inverting the function, and experimentally observed an exponential increase in running time as a function of the input length.
Starting Page 1
Ending Page 35
Page Count 35
File Format PDF
ISSN 19423454
e-ISSN 19423462
DOI 10.1145/2633602
Volume Number 6
Issue Number 3
Journal ACM Transactions on Computation Theory (TOCT)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-07-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword DPLL elimination rules One-way function Average preimage size Proof complexity Random bipartite graph Restricted backtracking algorithm
Content Type Text
Resource Type Article
Subject Computational Theory and Mathematics Theoretical Computer Science
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