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.