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计算机工程 ›› 2011, Vol. 37 ›› Issue (14): 12-17. doi: 10.3969/j.issn.1000-3428.2011.14.004

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基于敏感性分级的(αi, k)-匿名隐私保护

金 华 1,张志祥 1,2,刘善成 1,鞠时光 1   

  1. (1. 江苏大学计算机科学与通信工程学院,江苏 镇江 212013;2. 紫琅职业技术学院软件工程系,江苏 南通 226002)
  • 收稿日期:2011-03-10 出版日期:2011-07-20 发布日期:2011-07-20
  • 作者简介:金 华(1977-),男,讲师、博士研究生,主研方向:数据库安全,数据隐私保护;张志祥、刘善成,硕士研究生;鞠时光,教授、博士、博士生导师
  • 基金资助:

    国家自然科学基金资助项目(60773049);江苏省自然科学基金资助项目(BK2010192);江苏省中小企业技术创新基金资助项目(BC2008140)

(αi, k)-anonymity Privacy Preservation Based on Sensitivity Grading

JIN Hua 1, ZHANG Zhi-xiang 1,2, LIU Shan-cheng 1, JU Shi-guang 1   

  1. (1. School of Computer Science & Telecommunications Engineering, Jiangsu University, Zhenjiang 212013, China; 2. Department of Software Engineering, Zilang Vocational Technical College, Nantong 226002, China)
  • Received:2011-03-10 Online:2011-07-20 Published:2011-07-20

摘要:

(α, k)-匿名模型未考虑敏感属性不同取值间的敏感性差异,不能很好地抵御同质性攻击。同时传统基于泛化的实现方法存在效率低、信息损失量大等缺点。为此,提出一种基于敏感性分级的(αi, k)-匿名模型,考虑敏感值之间的敏感性差异,引入有损连接思想,设计基于贪心策略的(?i, k)-匿名聚类算法。实验结果表明,该模型能抵御同质性攻击,是一种有效的隐私保护方法。

关键词: 隐私保护, (αi, k)-匿名模型, 泛化, 有损连接, 同质性攻击

Abstract:

(α, k)-anonymity model can not thwart the homogeneity attack well because of the model ignoring the sensitive difference between sensitive attribute. It is achieved traditionally via generalization techniques. It also has some defects on efficiency and data distortion. So this paper proposes an improved (αi, k)-anonymity model. It considers the sensitive difference between sensitive attribute, and designs a (αi, k)-anonymity clustering algorithm based on greedy strategy recur to the idea of lossy join. Experimental results show that the proposed model can resist homogeneity attack and is an effective approach.

Key words: privacy preservation, (αi, k)-anonymity model, generalization, lossy join, homogeneity attack

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