基于属性分割的差分隐私异构多属性数据发布

针对现有多属性数据隐私发布方法无法兼顾属性的敏感性差异和计算效率低的问题, 提出了一种基于属性分割的差分隐私异构多属性数据发布方法HMPrivBayes. 首先, 设计了满足差分隐私的谱聚类算法分割原始数据集, 其中相似矩阵的生成借助于属性最大信息系数. 其次, 借助属性信息, 该方法使用满足差分隐私的改进贝叶斯网络构建算法分别为每个数据子集构建贝叶斯网络. 最后, 以属性归一化风险熵为权重分配隐私预算, 对贝叶斯网络提取的属性联合分布添加异构噪声扰动, 实现了异构多属性数据保护. 实验结果表明, HMPrivBayes可以在减少注入合成数据集中噪声量的同时, 提高合成数据计算效率.

Differentially Private Heterogeneous Multi-attribute Data Publication via Attribute Segmentation

Multi-attribute data privacy publication fails to balance the difference in attribute sensitivity and computational efficiency. For this reason, HMPrivBayes, a heterogeneous multi-attribute data publishing method with differential privacy based on attribute segmentation, is proposed. Firstly, the spectral clustering algorithm satisfying differential privacy is designed to segment the original data set, in which the similarity matrix is generated by the attribute maximum information coefficient. Secondly, with the help of attribute information, this method uses an improved Bayesian network construction algorithm to build Bayesian networks for each data subset. Finally, HMPrivBayes adds heterogeneous noise disturbance to the attribute joint distribution extracted from the Bayesian network to realize the protection of heterogeneous multi-attribute data, in which privacy budget is allocated based on the normalized risk entropy of attribute. The experimental results show that HMPrivBayes not only reduces the added noise but also improves the computational efficiency of synthetic data.