semi-homogenous generalization：improving homogenous generalization for privacy preservation in cloud computing.pdfVIP

He XM, Wang XS, Li D et al. Semi-homogenous generalization: Improving homogenous generalization for privacy preser- vation in cloud computing. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 31(6): 1124–1135 Nov. 2016. DOI 10.1007/s11390-016-1687-6 Semi-Homogenous Generalization: Improving Homogenous Generalization for Privacy Preservation in Cloud Computing Xian-Mang He 1, Xiaoyang Sean Wang 2, Member, CCF, ACM, IEEE, Dong Li 3, and Yan-Ni Hao 3 1School of Information Science and Engineering, Ningbo University, Ningbo 315211, China 2School of Computer Science, Fudan University, Shanghai 200433, China 3Information Center, National Natural Science Foundation of China, Beijing 100085, China E-mail: hexianmang@; xywangCS@; {lidong, haoyn}@ Received July 19, 2016; revised October 8, 2016. Abstract 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. Keywords privacy preservation, cloud computing, linking-based anonymization, semi-homogenous, homogenou