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A Fundamental Trade-Off between Communication Load and Recovery Threshold in Secure Distributed Computing
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데이터 보안을 보장하는 분산 컴퓨팅에서 통신량과 복구 한계치 사이의 이론적 Trade-Off

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Type
Academic journal
Author
Heecheol Yang (금오공과대학교)
Journal
Korea Institute Of Communication Sciences The Journal of Korean Institute of Communications and Information Sciences Vol.45 No.10 KCI Accredited Journals SCOPUS
Published
2020.10
Pages
1,804 - 1,815 (12page)
DOI
10.7840/kics.2020.45.10.1804

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A Fundamental Trade-Off between Communication Load and Recovery Threshold in Secure Distributed Computing
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Research history (2)

2020

A Fundamental Trade-Off between Communication Load and Recovery Threshold in Secure Distributed Computing
Heecheol Yang The Journal of Korean Institute of Communications and Information Sciences 2020.10 Academic journal
A Tradeoff between Communication Load and Straggling Effect in Distributed Computing with Data Security
Heecheol Yang Proceedings of Symposium of the Korean Institute of communications and Information Sciences 2020.02 Proceeding
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Abstract· Keywords

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In this paper, we consider a fundamental trade-off between communication load and recovery threshold in a distributed computing performing matrix multiplication where data security should be preserved. Firstly, we propose a new distributed matrix multiplication scheme in which multiple workers can collude to acquire information about input data, and derive the achievable pairs of communication load and recovery threshold. We also derive a fundamental limits on recovery threshold for given communication load in an information-theoretic sense. In addition, we reveal the distribution of the waiting time at the master in various distributed computing scenarios to show the effects of the tradeoff between communication load and recovery threshold.
#Distributed computing #data security #straggling effects

Contents

요약
ABSTRACT
Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 실험
Ⅳ. 결론
References

References (15)

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J. Dean and L. Barroso, “The tail at scale,” Commun. ACM, vol. 56, no. 2, pp. 74-80, Feb. 2013. google schola K. Lee, M. Lam, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran, “Speeding up distributed machine learning using codes,” IEEE Trans. Info. Theory, vol. 64, no. 3, pp. 1514-1529, Mar. 2018. google schola Q. Yu, M. A. Maddah-Ali, and A. S. Avestimehr, “Polynomial codes: an optimal design for high-dimensional coded matrix multiplication,” in Proc. 32th Annual Conf. NIPS, pp. 4403-4413, Dec. 2017. google schola S. Dutta, M. Fahim, F. Haddadpour, H. Jeong, V. Cadambe, and P. Grover, “On the optimal recovery threshold of coded matrix multiplication,” IEEE Trans. Info. Theory, vol. 66, no. 1, pp. 278-301, Jan. 2020. google schola Q. Yu, M. A. Maddah-Ali, and A. S. Avestimehr, “Straggler mitigation in distributed matrix multiplication: fundamental limits and optimal coding,” IEEE Trans. Info. Theory, vol. 66, no. 3, pp. 1920-1933, Mar. 2020. google schola

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UCI(KEPA) : I410-ECN-0101-2020-567-001314696