Journal of Frontiers of Computer Science and Technology ›› 2011, Vol. 5 ›› Issue (8): 740-750.

• 学术研究 • Previous Articles     Next Articles

Data Privacy Preservation for Dynamic Numerical Sensitive Attributes

HE Xianmang, CHEN Huahui, XIAO Yanghua, WANG Wei, SHI Bole   

  1. 1. School of Information Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, China 2. School of Computer Science and Technology, Fudan University, Shanghai 200433, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-08-01 Published:2011-08-01

动态数值敏感属性的数据隐私保护

何贤芒, 陈华辉, 肖仰华, 汪 卫, 施伯乐   

  1. 1. 宁波大学 信息科学与工程学院, 浙江 宁波 315211
    2. 复旦大学 计算机科学技术学院, 上海 200433

Abstract: A lately privacy preservation for dynamic data has attracted great attention. The concept of m-invariance was proposed and solved the problem of data privacy preservation for dynamic categorical sensitive attributes, but it made no progress for dynamic numerical sensitive attributes. This paper analyzes the problem of data privacy pre- servation for dynamic numerical sensitive attributes, and then proposes the concept of m-increment and the corre-sponding generalization algorithm to solve the problem. Finally, the experiments demonstrate the effectiveness and efficiency of the method.

Key words: privacy preservation, k-anonymity, m-invariance

摘要: 目前动态数据的隐私保护引起了人们的广泛关注。m-invariance概念的提出, 比较好地解决了动态类别敏感属性的数据隐私保护问题, 但对于动态数值敏感属性却未取得任何进展。描述了动态数值敏感属性的数据隐私保护问题, 提出了解决该问题的m-increment概念及其泛化算法, 并通过实验数据说明了算法的实用性和效率。

关键词: 隐私保护, k-匿名, m-不变性