中文摘要：

基于匿名化技术的理论基础,采用DBSCAN聚类算法对数据记录进行聚类,实现将个体记录匿名化隐藏于一组记录中。为提高隐私保护程度,对匿名化划分的数据添加拉普拉斯噪声,扰动个体数据真实值,以实现差分隐私保护模型的要求。通过聚类,分化查询函数敏感性,提高数据可用性。对算法隐私性进行证明,并实验说明发布数据的可用性。

英文摘要：

Based on the theory of anonymization, the DBSCAN method was applied to divide all the data records into different groups to cover individuals. To provide privacy enhancement, the Laplace noise was added to the anonymized partitioned data to perturb the real value of data record so that the requirements of differential privacy model were satisfied. With the clustering operation, the sensitivity of the query function has been partitioned to improve data utility. The proof of privacy has been given and experimental results have been provided to evaluate the utility of the released data.