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Lower Bound on Average-Case Complexity of Inversion of Goldreich’s Function by Drunken Backtracking Algorithms

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Computer Science – Theory and Applications (CSR 2010)

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Abstract

We prove an exponential lower bound on the average time of inverting Goldreich’s function by drunken [AHI05] backtracking algorithms; therefore we resolve the open question stated in [CEMT09]. The Goldreich’s function [Gol00] has n binary inputs and n binary outputs. Every output depends on d inputs and is computed from them by the fixed predicate of arity d. Our Goldreich’s function is based on an expander graph and on the nonliniar predicates of a special type. Drunken algorithm is a backtracking algorithm that somehow chooses a variable for splitting and randomly chooses the value for the variable to be investigated at first. Our proof technique significantly simplifies the one used in [AHI05] and in [CEMT09].

Partially supported by RFBR grants 08-01-00640 and 09-01-12137-ofi_m, the Fundamental research program of the Russian Academy of Sciences, the president of Russia grant “Leading Scientific Schools” NSh-4392.2008.1 and by Federal Target Programme “Scientific and scientific-pedagogical personnel of the innovative Russia” 2009-2013.

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References

  1. Alekhnovich, M., Ben-Sasson, E., Razborov, A.A., Wigderson, A.: Pseudorandom generators in propositional proof complexity. In: FOCS ’00: Proceedings of the 41st Annual Symposium on Foundations of Computer Science, Washington, DC, USA, p. 43. IEEE Computer Society, Los Alamitos (2000)

    Chapter  Google Scholar 

  2. Alekhnovich, M., Hirsch, E.A., Itsykson, D.: Exponential lower bounds for the running time of DPLL algorithms on satisfiable formulas. J. Autom. Reason. 35(1-3), 51–72 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  3. Ben-Sasson, E., Wigderson, A.: Short proofs are narrow — resolution made simple. Journal of ACM 48(2), 149–169 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  4. Cook, J., Etesami, O., Miller, R., Trevisan, L.: Goldreich’s one-way function candidate and myopic backtracking algorithms. In: Reingold, O. (ed.) Theory of Cryptography. LNCS, vol. 5444, pp. 521–538. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  5. Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving. Communications of the ACM 5, 394–397 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  6. Davis, M., Putnam, H.: A computing procedure for quantification theory. Journal of the ACM 7, 201–215 (1960)

    Article  MATH  MathSciNet  Google Scholar 

  7. Eén, N., Biere, A.: Effective preprocessing in SAT through variable and clause elimination. Theory and Applications of Satisfiability Testing, 61–75 (2005)

    Google Scholar 

  8. Een, N., Sorensson, N.: An extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004)

    Google Scholar 

  9. Goldreich, O.: Candidate one-way functions based on expander graphs. Technical Report 00-090, Electronic Colloquium on Computational Complexity (2000)

    Google Scholar 

  10. Hoory, S., Linial, N., Wigderson, A.: Expander graphs and their applications. Bulletin of the American Mathematical Society 43, 439–561 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  11. Mironov, I., Zhang, L.: Applications of SAT solvers to cryptanalysis of hash functions. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 102–115. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Nisan, N., Wigderson, A.: Hardness vs. randomness. Journal of Computer and System Sciences 49, 149–167 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  13. Tseitin, G.S.: On the complexity of derivation in the propositional calculus. Zapiski nauchnykh seminarov LOMI 8, 234–259 (1968); English translation of this volume: Consultants Bureau, N.Y., pp. 115–125 (1970)

    MATH  Google Scholar 

  14. Urquhart, A.: Hard examples for resolution. J. ACM 34(1), 209–219 (1987)

    Article  MATH  MathSciNet  Google Scholar 

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Itsykson, D. (2010). Lower Bound on Average-Case Complexity of Inversion of Goldreich’s Function by Drunken Backtracking Algorithms. In: Ablayev, F., Mayr, E.W. (eds) Computer Science – Theory and Applications. CSR 2010. Lecture Notes in Computer Science, vol 6072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13182-0_19

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  • DOI: https://doi.org/10.1007/978-3-642-13182-0_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13181-3

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