›› 2016, Vol. 31 ›› Issue (6): 1124-1135.doi: 10.1007/s11390-016-1687-6

Special Issue: Theory and Algorithms

• Special Section on Data-Driven Design for Edge Network and Edge Cloud • Previous Articles     Next Articles

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

Xian-Mang He1, Xiaoyang Sean Wang2, Member, CCF, ACM, IEEE, Dong Li3, Yan-Ni Hao3   

  1. 1 School of Information Science and Engineering, Ningbo University, Ningbo 315211, China;
    2 School of Computer Science, Fudan University, Shanghai 200433, China;
    3 Information Center, National Natural Science Foundation of China, Beijing 100085, China
  • Received:2016-07-19 Revised:2016-10-08 Online:2016-11-05 Published:2016-11-05
  • Supported by:

    This work was supported in part by the National Natural Science Foundation of China under Grant Nos. U1509213, 61672303, 61370080, the Postdoctoral Science Foundation of China under Grant No. 2013M540323, and the Shanghai Municipal Science and Technology Commission Project under Grant No. 16DZ1100200.

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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