计算机工程与应用 ›› 2012, Vol. 48 ›› Issue (20): 136-141.

• 网络、通信、安全 • 上一篇    下一篇

多维敏感属性隐私保护数据发布方法

王胜和1,王佳俊2,刘腾腾2,倪巍伟2   

  1. 1.安徽公安职业学院 公安科技系,合肥 230031
    2.东南大学 计算机科学与工程学院,南京 210096
  • 出版日期:2012-07-11 发布日期:2012-07-10

Privacy-preserving data publishing method for dataset with multi-dimensional sensitive attributes

WANG Shenghe1, WANG Jiajun2, LIU Tengteng2, NI Weiwei2   

  1. 1.Department of Public Security Technology, Anhui Public Security Professional College, Hefei 230031, China
    2.School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
  • Online:2012-07-11 Published:2012-07-10

摘要: 在匿名数据发布中,当敏感属性为多维时,攻击者有可能能够获取一维或几维敏感属性信息,并且结合准标识符信息对其他敏感属性进行推理攻击。针对此问题提出(Dou-l)-匿名模型,更好地保护了敏感信息。基于多维桶和分解思想,提出(Dou-l)-匿名算法,使得即便攻击者掌握了部分敏感数据,仍然能较好地保护其他敏感属性数据的隐私安全性。实际数据实验证明,算法可以较好地均衡发布数据的安全性和可用性。

关键词: 隐私保护, 多敏感属性, 数据发布, 背景知识

Abstract: When publishing data with multiple sensitive attributes, an adversary may be able to get some sensitive attribute information, attack other sensitive attribute information through a combination of this background knowledge with quasi-identifier information. To avoid this problem, a formal multiple sensitive attributes data publication model is defined, named (Dou-l)-anonymity. The corresponding (Dou-l)-anonymity implementation algorithm is proposed based on the idea of multi-sensitive bucketization and lossy join. The findings are verified by experiments with real data.

Key words: privacy preserving, multiple sensitive attributes, data publishing, background knowledge