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Lower Bounds on the Query Complexity of Non-uniform and Adaptive Reductions Showing Hardness Amplification

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Abstract

Hardness amplification results show that for every Boolean function f, there exists a Boolean function Amp(f) such that if every size s circuit computes f correctly on at most a 1 − δ fraction of inputs, then every size s′ circuit computes Amp(f) correctly on at most a \({1/2+\epsilon}\) fraction of inputs. All hardness amplification results in the literature suffer from “size loss” meaning that \({s' \leq \epsilon \cdot s}\). We show that proofs using “non-uniform reductions” must suffer from such size loss.

A reduction is an oracle circuit \({R^{(\cdot)}}\) which given oracle access to any function D that computes Amp(f) correctly on a \({1/2+\epsilon}\) fraction of inputs, computes f correctly on a 1 − δ fraction of inputs. A non-uniform reduction is allowed to also receive a short advice string that may depend on both f and D. The well-known connection between hardness amplification and list-decodable error-correcting codes implies that reductions showing hardness amplification cannot be uniform for \({\epsilon < 1/4}\). We show that every non-uniform reduction must make at least \({\Omega(1/\epsilon)}\) queries to its oracle, which implies size loss. Our result is the first lower bound that applies to non-uniform reductions that are adaptive, whereas previous bounds by Shaltiel & Viola (SICOMP 2010) applied only to non-adaptive reductions. We also prove similar bounds for a stronger notion of “function-specific” reductions in which the reduction is only required to work for a specific function f.

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References

  • A. Akavia, O. Goldreich, S. Goldwasser & D. Moshkovitz (2006). On basing one-way functions on NP-hardness. In Proceedings of the 38th Annual ACM Symposium on Theory of Computing, 701–710.

  • A. Atserias (2006). Distinguishing SAT from Polynomial-Size Circuits, through Black-Box Queries. In IEEE Conference on Computational Complexity, 88–95.

  • Babai L., Fortnow L., Nisan N., Wigderson A. (1993) BPP Has Subexponential Time Simulations Unless EXPTIME has Publishable Proofs. Computational Complexity 3: 307–318

    Article  MATH  MathSciNet  Google Scholar 

  • A. Bogdanov & M. Safra (2007). Hardness Amplification for Errorless Heuristics. In Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science, 418–426.

  • Bogdanov A., Trevisan L. (2006) OnWorst-Case to Average-Case Reductions for NP Problems. SIAM J. Comput. 36(4): 1119–1159

    Article  MATH  MathSciNet  Google Scholar 

  • Feigenbaum J., Fortnow L. (1993) Random-Self-Reducibility of Complete Sets. SIAM J. Comput. 22(5): 994–1005

    Article  MATH  MathSciNet  Google Scholar 

  • O. Goldreich, N. Nisan & A. Wigderson (2011). On Yao’s XORLemma. In Studies in Complexity and Cryptography, Oded Goldreich, editor, volume 6650 of Lecture Notes in Computer Science, 273–301. Springer. ISBN 978-3-642-22669-4.

  • S. Goldwasser, D. Gutfreund, A. Healy, T. Kaufman & G. N. Rothblum (2007). Verifying and decoding in constant depth. In Proceedings of the 39th Annual ACM Symposium on Theory of Computing, 440–449.

  • Gopalan P., Guruswami V. (2011) Hardness amplification within NP against deterministic algorithms. J. Comput. Syst. Sci. 77(1): 107–121

    Article  MATH  MathSciNet  Google Scholar 

  • Guruswami V., Kabanets V. (2008) Hardness Amplification via Space-Efficient Direct Products. Computational Complexity 17(4): 475–500

    Article  MATH  MathSciNet  Google Scholar 

  • D. Gutfreund & G. Rothblum (2008). The Complexity of Local List Decoding. In Proceedings of the 12th Intl. Workshop on Randomization and Computation.

  • Gutfreund D., Shaltiel R., Ta-Shma A. (2007) If NP Languages are Hard on the Worst-Case, Then it is Easy to Find Their Hard Instances. Computational Complexity 16(4): 412–441

    Article  MATH  MathSciNet  Google Scholar 

  • D. Gutfreund & A. Ta-Shma (2007). Worst-Case to Average-Case Reductions Revisited. In Proceedings of the 11th Intl. Workshop on Randomization and Computation, 569–583.

  • D. Gutfreund & S. P. Vadhan (2008). Limitations of Hardness vs. Randomness under Uniform Reductions. In Proceedings of the 12th Intl. Workshop on Randomization and Computation, 469–482.

  • Healy A., Vadhan S.P., Viola E. (2006) Using Nondeterminism to Amplify Hardness. SIAM J. Comput. 35(4): 903–931

    Article  MATH  MathSciNet  Google Scholar 

  • R. Impagliazzo (1995). Hard-Core Distributions for Somewhat Hard Problems. In Proceedings of the 36th Annual IEEE Symposium on Foundations of Computer Science, 538–545.

  • Impagliazzo R., Jaiswal R., Kabanets V. (2009a) Approximate List-Decoding of Direct Product Codes and Uniform Hardness Amplification. SIAM J. Comput. 39(2): 564–605

    Article  MATH  MathSciNet  Google Scholar 

  • Impagliazzo R., Jaiswal R., Kabanets V. (2009b) Chernoff-Type Direct Product Theorems. J. Cryptology 22(1): 75–92

    Article  MATH  MathSciNet  Google Scholar 

  • Impagliazzo R., Jaiswal R., Kabanets V., Wigderson A. (2010) Uniform Direct Product Theorems: Simplified, Optimized, and Derandomized. SIAM J. Comput. 39(4): 1637–1665

    Article  MathSciNet  Google Scholar 

  • R. Impagliazzo & A. Wigderson (1997). PBPP if E Requires Exponential Circuits: Derandomizing the XOR Lemma. In Proceedings of the 29th Annual ACM Symposium on Theory of Computing, 220–229.

  • Impagliazzo R., Wigderson A. (2001) Randomness vs Time: Derandomization under a Uniform Assumption. J. Comput. Syst. Sci. 63(4): 672–688

    Article  MATH  MathSciNet  Google Scholar 

  • Klivans A., Servedio R.A. (2003) Boosting and Hard-Core Sets. Machine Learning 53(3): 217–238

    Article  Google Scholar 

  • Levin L.A. (1987) One-way functions and pseudorandom generators. Combinatorica 7(4): 357–363

    Article  MATH  MathSciNet  Google Scholar 

  • R. Lipton (1991). New Directions in Testing. In Proceedings of DIMACS Workshop on Distributed Computing and Cryptography, volume 2, 191–202. ACM/AMS.

  • Lu C.-J., Tsai S.-C., Wu H.-L. (2008) On the Complexity of Hardness Amplification. IEEE Transactions on Information Theory 54(10): 4575–4586

    Article  MathSciNet  Google Scholar 

  • Lu C.-J., Tsai S.-C., Wu H.-L. (2011) Complexity of Hard-Core Set Proofs. Computational Complexity 20(1): 145–171

    Article  MATH  MathSciNet  Google Scholar 

  • O’Donnell R. (2004) Hardness amplification within NP. J. Comput. Syst. Sci. 69(1): 68–94

    Article  MATH  MathSciNet  Google Scholar 

  • Raz R. (1998) A Parallel Repetition Theorem. SIAM J. Comput. 27(3): 763–803

    Article  MATH  MathSciNet  Google Scholar 

  • O. Reingold, L. Trevisan & S. P. Vadhan (2004). Notions of Reducibility between Cryptographic Primitives. In Proceedings of the 1st Theory of Cryptography Conference, 1–20.

  • Shaltiel R., Umans C. (2005) Simple extractors for all minentropies and a new pseudorandom generator. J. ACM 52(2): 172–216

    Article  MathSciNet  Google Scholar 

  • Shaltiel R., Viola E. (2010) Hardness Amplification Proofs Require Majority. SIAM J. Comput. 39(7): 3122–3154

    Article  MATH  MathSciNet  Google Scholar 

  • Sudan M., Trevisan L., Vadhan S.P. (2001) Pseudorandom Generators without the XOR Lemma. J. Comput. Syst. Sci. 62(2): 236–266

    Article  MATH  MathSciNet  Google Scholar 

  • L. Trevisan (2003). List-Decoding Using The XOR Lemma. In Proceedings of the 44th Symposium on Foundations of Computer Science, 126–135.

  • L. Trevisan (2004). Some applications of coding theory in computational complexity. In Complexity of computations and proofs, volume 13 of Quad. Mat., 347–424. Dept. Math., Seconda Univ. Napoli, Caserta.

  • L. Trevisan (2005). On uniform amplification of hardness in NP. In Proceedings of the 37th Annual ACM Symposium on Theory of Computing, 31–38.

  • Trevisan L., Vadhan S. (2007) Pseudorandomness and Average-Case Complexity Via Uniform Reductions. Computational Complexity 16(4): 331–364

    Article  MATH  MathSciNet  Google Scholar 

  • Viola E. (2005a) The complexity of constructing pseudorandom generators from hard functions. Computational Complexity 13(3-4): 147–188

    Article  MathSciNet  Google Scholar 

  • E. Viola (2005b). On Constructing Parallel Pseudorandom Generators from One-Way Functions. In IEEE Conference on Computational Complexity, 183–197.

  • T. Watson (2011). Query Complexity in Errorless Hardness Amplification. In Proceedings of the 15th Intl. Workshop on Randomization and Computation, 688–699.

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Correspondence to Ronen Shaltiel.

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Artemenko, S., Shaltiel, R. Lower Bounds on the Query Complexity of Non-uniform and Adaptive Reductions Showing Hardness Amplification. comput. complex. 23, 43–83 (2014). https://doi.org/10.1007/s00037-012-0056-2

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