Semi-Homogenous Generalization: Improving Homogenous Generalization for Privacy Preservation in Cloud Computing

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• 作者：Xian-Mang He ; Xiaoyang Sean Wang ; Dong Li…
• 关键词：privacy preservation ; cloud computing ; linking ; based anonymization ; semi ; homogenous ; homogenous generalization
• 刊名：Journal of Computer Science and Technology
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：31
• 期：6
• 页码：1124-1135
• 全文大小：315 KB
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Software Engineering
  Theory of Computation
  Data Structures, Cryptology and Information Theory
  Artificial Intelligence and Robotics
  Information Systems Applications and The Internet
  Chinese Library of Science
  
• 出版者：Springer Boston
• ISSN：1860-4749
• 卷排序：31

文摘

Data security is one of the leading concerns and primary challenges for cloud computing. This issue is getting more and more serious with the development of cloud computing. However, the existing privacy-preserving data sharing techniques either fail to prevent the leakage of privacy or incur huge amounts of information loss. In this paper, we propose a novel technique, termed as linking-based anonymity model, which achieves K-anonymity with quasi-identifiers groups (QI-groups) having a size less than K. In the meanwhile, a semi-homogenous generalization is introduced to be against the attack incurred by homogenous generalization. To implement linking-based anonymization model, we propose a simple yet efficient heuristic local recoding method. Extensive experiments on real datasets are also conducted to show that the utility has been significantly improved by our approach compared with the state-of-the-art methods.