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统计数据库中保持隐私的数据扰动方法的研究 被引量：3

Basic Research on Data Perturbation Methods for Privacy in Statistical Database
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摘要 随着统计数据库的广泛应用和不断发展,统计数据库中隐私问题显得越来越重要.针对这方面问题,目前已经出现了许多不同的统计数据库的安全控制方法.数据扰动方法是其中一种相对较好的安全控制方法.它主要是通过添加一种噪音矢量对原数据进行修改,从而达到秘密数据保护的目的.主要综述了近年来出现的各种数据扰动方法,对它们各自的特点作了一定的比较分析.并对EGADP方法的思想从数学理论方面进行了推导验证,这点有助于开拓对数据扰动新方法研究的思路.最后就数据扰动方法的相关问题作了总结和展望.
作者 黄伟伟 柏文阳
机构地区 南京大学计算机软件新技术国家重点实验室
出处 《计算机研究与发展》 EI CSCD 北大核心 2006年第z3期289-294,共6页 Journal of Computer Research and Development
基金 国家"八六三"高技术研究发展计划基金项目(2002AA141091)
关键词 统计数据库 隐私问题 数据扰动
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献12

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同被引文献30

  • 1罗文俊,李祥.多方安全矩阵乘积协议及应用[J].计算机学报,2005,28(7):1230-1235. 被引量：34
  • 2Vaidya Jaideep, Yu Hwanjo, Jiang Xiaoqian. Privacy-preserving SVM classification [J]. Knowledge and Information Systems, 2008, 14(2): 161-178.
  • 3Yu Hwanjo, Vaidya Jaideep, Jiang Xiaoqian. Privacy-preserving SVM classification on vertically partitioned data [C] //Advances in Knowledge Discovery and Data Mining. Berlin: Springer, 2006:647-656.
  • 4Verykios V S, Bertino E, Fovino I N, et al. State-of-the-art in privacy preserving data mining [C]//SIGMOD Record. New York: ACM, 2004: 50-57.
  • 5A. grawal D, Aggarwal C C. On the design and quantification of privacy preserving data mining algorithms [C] //Proc of the 20th ACM SIGMOD-SIGACT-SIGART Symp on Principles of Database Systems. New York: ACM, 2001: 247-255.
  • 6Goethals B, Laur S, Lipmaa H, et al. On private scalar product computation for privacy preserving data mining [C]//Proc of the 7th Int Conf on Information Security and Cryptology(ICISC 2004). Berlin: Springer, 2004:104-120.
  • 7Aggarwal C C, Yu P S. A condensation approach to privacy preserving data mining [C]//Advances in Database Technology(EDBT 2904). Berlin: Springer, 2004:183-199.
  • 8Yu Hwanjo, Jiang Xiaoqian, Vaidya Jaideep. Privacy preserving SVM using nonlinear kernels on horizontally partitioned data [C] //Proc of the 2006 ACM Syrup on Applied computing. New York: ACM, 2006:603-610.
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  • 10Huang K, Yang H, King I, et al. The minimum error minimax probability machine [J]. The Journal of Machine Learning Research, 2004, 5(10) : 1253-1286.

引证文献3

二级引证文献7

计算机研究与发展
2006年 第z3期

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