One-Sided Error Communication Complexity of Gap Hamming Distance

Authors Egor Klenin, Alexander Kozachinskiy



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Author Details

Egor Klenin
  • Lomonosov Moscow State University, Moscow, Russia, Moscow, 1 Leninskiye Gory, Russia
Alexander Kozachinskiy
  • National Research University Higher School of Economics, Moscow, Russia, Moscow, 3 Kochnovsky Proezd, Russia

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Egor Klenin and Alexander Kozachinskiy. One-Sided Error Communication Complexity of Gap Hamming Distance. In 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 117, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.MFCS.2018.7

Abstract

Assume that Alice has a binary string x and Bob a binary string y, both strings are of length n. Their goal is to output 0, if x and y are at least L-close in Hamming distance, and output 1, if x and y are at least U-far in Hamming distance, where L < U are some integer parameters known to both parties. If the Hamming distance between x and y lies in the interval (L, U), they are allowed to output anything. This problem is called the Gap Hamming Distance. In this paper we study public-coin one-sided error communication complexity of this problem. The error with probability at most 1/2 is allowed only for pairs at Hamming distance at least U. In this paper we determine this complexity up to factors logarithmic in L. The protocol we construct for the upper bound is simultaneous.

Subject Classification

ACM Subject Classification
  • Theory of computation → Communication complexity
Keywords
  • Communication Complexity
  • Gap Hamming Distance
  • one-sided error

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References

  1. Eric Blais, Joshua Brody, and Kevin Matulef. Property testing lower bounds via communication complexity. Computational Complexity, 21(2):311-358, 2012. Google Scholar
  2. Joshua Brody and Amit Chakrabarti. A multi-round communication lower bound for gap hamming and some consequences. In Computational Complexity, 2009. CCC'09. 24th Annual IEEE Conference on, pages 358-368. IEEE, 2009. Google Scholar
  3. Joshua Brody, Amit Chakrabarti, Oded Regev, Thomas Vidick, and Ronald De Wolf. Better gap-hamming lower bounds via better round elimination. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, pages 476-489. Springer, 2010. Google Scholar
  4. Joshua Brody and David P Woodruff. Streaming algorithms with one-sided estimation. In APPROX-RANDOM, pages 436-447. Springer, 2011. Google Scholar
  5. Harry Buhrman, Richard Cleve, and Avi Wigderson. Quantum vs. classical communication and computation. In Proceedings of the thirtieth annual ACM symposium on Theory of computing, pages 63-68. ACM, 1998. Google Scholar
  6. Amit Chakrabarti and Oded Regev. An optimal lower bound on the communication complexity of gap-hamming-distance. SIAM Journal on Computing, 41(5):1299-1317, 2012. Google Scholar
  7. Gérard Cohen, Iiro Honkala, Simon Litsyn, and Antoine Lobstein. Covering codes, volume 54. Elsevier, 1997. Google Scholar
  8. Dmitry Gavinsky, Julia Kempe, and Ronald de Wolf. Quantum communication cannot simulate a public coin. arXiv preprint quant-ph/0411051, 2004. Google Scholar
  9. Wei Huang, Yaoyun Shi, Shengyu Zhang, and Yufan Zhu. The communication complexity of the hamming distance problem. Information Processing Letters, 99(4):149-153, 2006. Google Scholar
  10. Thathachar S Jayram, Ravi Kumar, and D Sivakumar. The one-way communication complexity of hamming distance. Theory of Computing, 4(1):129-135, 2008. Google Scholar
  11. Eyal Kushilevitz, Rafail Ostrovsky, and Yuval Rabani. Efficient search for approximate nearest neighbor in high dimensional spaces. SIAM Journal on Computing, 30(2):457-474, 2000. Google Scholar
  12. Alexander A Sherstov. The communication complexity of gap hamming distance. Theory of Computing, 8(1):197-208, 2012. Google Scholar
  13. Thomas Vidick. A concentration inequality for the overlap of a vector on a large set, with application to the communication complexity of the gap-hamming-distance problem. Chicago Journal of Theoretical Computer Science, 1, 2012. Google Scholar
  14. David Woodruff. Optimal space lower bounds for all frequency moments. In Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms, pages 167-175. Society for Industrial and Applied Mathematics, 2004. Google Scholar
  15. Andrew Chi-Chih Yao. On the power of quantum fingerprinting. In Proceedings of the thirty-fifth annual ACM symposium on Theory of computing, pages 77-81. ACM, 2003. Google Scholar
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