中文摘要：

数据安全为云计算是领先的担心和主要挑战之一。这个问题与云计算的发展正在变得越来越严肃。然而，存在保存隐私的数据分享技术也没能阻止隐私的漏或招致信息损失的巨大的数量。在这份报纸，我们建议一种新奇技术，作为基于连接的匿名模型称为，它与有一种尺寸的伪标识符组(QI 组) 一起完成 K 匿名不到 K。同时，半同质的归纳被介绍对同质的归纳招致的攻击。为了实现基于连接的 anonymization，当模特儿，我们建议一个简单还有效的启发式的本地人重新代码方法。真实数据集的广泛的实验也被进行证明实用程序被我们的途径显著地与最先进的方法相比改进了。

英文摘要：

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.