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Xian-Mang He, Xiaoyang Sean Wang, Dong Li, Yan-Ni Hao. Semi-Homogenous Generalization:Improving Homogenous Generalization for Privacy Preservation in Cloud Computing[J]. Journal of Computer Science and Technology, 2016, 31(6): 1124-1135. DOI: 10.1007/s11390-016-1687-6
Citation: Xian-Mang He, Xiaoyang Sean Wang, Dong Li, Yan-Ni Hao. Semi-Homogenous Generalization:Improving Homogenous Generalization for Privacy Preservation in Cloud Computing[J]. Journal of Computer Science and Technology, 2016, 31(6): 1124-1135. DOI: 10.1007/s11390-016-1687-6

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

  • 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.
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