王克 戴一奇. 统计分布的多方保密计算[J]. 计算机研究与发展, 2010, 47(2): 201-206.
 引用本文: 王克 戴一奇. 统计分布的多方保密计算[J]. 计算机研究与发展, 2010, 47(2): 201-206.
Wang Ke and Dai Yiqi. Secure Multiparty Computation of Statistical Distribution[J]. Journal of Computer Research and Development, 2010, 47(2): 201-206.
 Citation: Wang Ke and Dai Yiqi. Secure Multiparty Computation of Statistical Distribution[J]. Journal of Computer Research and Development, 2010, 47(2): 201-206.

## Secure Multiparty Computation of Statistical Distribution

• 摘要: 多方保密的概念是姚期智教授首先提出的，是计算网络计算环境中隐私保护的关键技术，在密码学中占有重要的地位，是构造许多密码学协议的基本模块，是国际密码学界近年来研究的热点问题.这方面国内外的学者进行了大量的研究，已经取得了许多理论成果与实用成果，但还有许多应用问题需要研究.介绍了多方保密计算方面的研究现状和一些需要研究的问题，研究了统计工作中所遇到的保密问题，主要解决在统计工作中经常遇到的统计分布的保密计算问题，基于计算离散对数困难性假设，运用严格的逻辑推理方法，提出了该问题的3个多方保密计算方案，并用模拟范例证明了方案的保密性.这样的问题尚没有见到研究报道，解决方案对于实际工作中的保密统计计算有重要的意义，它们可以用于保护统计过程中被统计对象的各种数据的保密，从而使被统计者不用担心隐私的泄漏，使所获得的数据更为可靠，更具有参考价值.

Abstract: Secure multi-party computation is now a crucial privacy preserving technology in network computing environment, and it plays an important role in cryptography. It is a basic block to construct other cryptographic protocol, and is recently a research focus in international cryptographic community. The researchers from home and abroad have made extensive researches on secure multi-party computations, and a lot of theoretical and practical achievements have been obtained, and yet a lot of practical problems need to be further studied. The authors first briefly introduce the state of the art in the study of secure multi-party computation, and some problems need to be further studied. Secondly, they study the secure problems that are encountered in statistics. Aiming at privacy preserving computing of statistical distribution, which is frequently encountered in statistics, and based on the intractability of computing discrete logarithm and using rigorous logic, three solutions are proposed to this problem. The privacy preserving property of these solutions are proved by simulation paradigm. The study of this problem has not been read in the literature. These solutions are of great importance in practical privacy preserving statistical computation. They can be used to protect the privacy of the informant, so that the informant need not worry about the leakage of their privacy. This makes the statistical results be more reliable and have more reference value.

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