Novel Privacy-Protecting Distributed Computation Model and Its Applications

余智欣 1; 2; 黄天戍 1; 杨乃扩 2; 汪阳 3

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Novel Privacy-Protecting Distributed Computation Model and Its Applications

更新时间：2025-12-08

- Novel Privacy-Protecting Distributed Computation Model and Its Applications
- Vol. 41, Issue 8, Pages: 954-958(2007)
- 作者机构：
  
  1. 武汉大学电子与信息学院,武汉,430079
  2. 曼彻斯特大学计算机学院, PL,曼彻斯特,英国,M139
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM73;TP393
- Online First：10 August 2007，
  
  Published：2007
- 稿件说明：
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余智欣 1, 2, 黄天戍 1, et al. Novel Privacy-Protecting Distributed Computation Model and Its Applications[J]. 2007, 41(8): 954-958.
DOI：

余智欣 1, 2, 黄天戍 1, et al. Novel Privacy-Protecting Distributed Computation Model and Its Applications[J]. 2007, 41(8): 954-958. DOI：

摘要

针对分布式数据共享及计算中的隐私保护问题

提出了一种适用于大规模分布式环境的隐私保护计算模型(PPCMLS)

该模型的核心为隐私安全模块

其将计算划分为本地计算和全局计算.通过综合运用同态加密、安全点积协议、数据随机扰乱算法等多种安全技术

在实现了多个节点在一个互不信任的分布式环境下合作计算的同时

任何节点无法获取其他节点的隐私信息及敏感中间计算结果.据此

又给出了基于该模型的分布式隐私保护方差计算、分布式隐私保护数据聚类算法.安全及动态性分析结果表明

该模型及其应用算法既可保证隐私数据的安全性

又避免了繁琐的一对多的交互加密过程

并在节点变化时

恢复计算仅涉及到变化的节点和构成隐私安全模块的3个节点

从而满足了大规模分布式环境所要求的高效性和良好的动态适应性.

Abstract

For protecting the privacy in the large-scale distributed database sharing and computation

a generic privacy-protecting computation model supporting the privacy-protecting data computation and analysis is proposed. The key of this model is the ‘privacy security module' which divides the distributed computation into ‘local computation' and ‘global computation'. By using homomorphic encryption

secure product protocol

random permutation and some other secure technologies comprehensively

the model achieves distributed data computations without compromising the privacy of both data record and sensitive intermediate computation result. Two privacy-protecting distributed algorithms based on this model

namely

the privacy-protecting distributed variance algorithm and privacy-preserving distributed k-means clustering algorithm

have also been introduced. The security and dynamic behavior analysis show that the proposed model and algorithms doesn't require any one-to-all interactions encryption

and the recovery computation only involves the changed nodes and the three nodes composing privacy security module when nodes change. So it's suitable for large-scale and dynamic distributed environments.

关键词

Keywords

references

Vaidya J, Clifton C. Privacy-preserving data mining: why, how, and when [J]. Security and Privacy Magazine, 2004, 2(6):19-27.

Estivill-Castro V, Brankovic L. Data swapping: balancing privacy against precision in mining for logic rules [C]∥Proceedings of the First Conference in Data Warehousing and Knowledge Discovery. Berlin: Springer-Verlag, 1999:389-398.

Kantarcioglu M, Clifon C. Privacy-preserving distributed mining of association rules on horizontally partitioned data [J]. IEEE Transaction on Knowledge and Data Engineering, 2004, 16(9):1026-1037.

Bresson E, Catalano D, Pointcheval D. A simple public key cryptosystem with a double trapdoor decryption mechanism and its applications[C]∥Proceedings of 9th International Conference on the Theory and Application of Cryptology and Information Security. Berlin: Springer-Verlag, 2003:37-54.

Yao A C. Protocols for secure computation [C]∥Proceedings of 23rd Annual IEEE Symposium on Foundations of Computer Science. Piscataway, USA: IEEE, 1982:160-164.

孙中伟, 冯登国, 武传坤. 基于加同态公钥密码体制的匿名数字指纹方案[J].软件学报,2005,16(10):1816-1821.

Sun Zhongwei, Feng Dengguo, Wu Chuankun. An anonymous fingerprinting scheme based on additively homomorphic public key cryptosystem [J]. Journal of Software, 2005, 16(10):1816-1821.

Phillip J C. Algorithm 383: permutations of a set with repetitions [J]. Communications of the ACM, 1970, 13(6):368-369.

Du W L, Atallah M J. Privacy-preserving cooperative statistical analysis[C]∥Proceedings of 17th Annual Computer Security Applications Conference. Piscataway, USA:IEEE, 2001:102-110.

Estivill-Castro V. Private representative-based clustering for vertically partitioned data [C]∥Proceedings of the 5th Mexican International Conference on Computer Science. Piscataway, USA: IEEE, 2004:160-167.

Vaidya J, Clifton C. Research track: privacy-preserving k-means clustering over vertically partitioned data[C]∥Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2003:206-215.

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