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

• Published in: Journal of computer science and technology 2016-11, Vol.31 (6), p.1124-1135
• Main Authors: He, Xian-Mang, Wang, Xiaoyang Sean, Li, Dong, Hao, Yan-Ni
• Format: Article
• Language: English
• Subjects: Age; Algorithms; Analysis; Artificial Intelligence; Bronchitis; Cloud computing; Computer information security; Computer Science; Data retrieval; Data Structures and Information Theory; Emphysema; Information sharing; Information Systems Applications (incl.Internet); Pneumonia; Privacy; Publishing; Regular Paper; Software Engineering; State of the art; Studies; Theory of Computation; Utilities; 信息丢失; 共享技术; 均匀; 实用程序; 数据安全; 编码方法; 计算; 隐私保护
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-016-1687-6

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.