面向高维数据发布的个性化差分隐私算法

在高维数据隐私发布过程中,差分隐私预算大小直接影响噪音的添加.针对不能合理地为多个相对独立的低维属性集合合理分配隐私预算,进而影响合成发布数据集的安全性和可用性,提出一种个性化隐私预算分配算法(PPBA).引入最大支撑树和属性节点权重值降低差分隐私指数机制挑选属性关系对的候选空间,提高贝叶斯网络精确度,提出使用贝叶斯网络中节点动态权重值衡量低维属性集合的敏感性排序.根据发布数据集安全性和可用性的个性化需求,个性化设置差分隐私预算分配比值常数q值,实现对按敏感性排序的低维属性集合个性化分配拉普拉斯噪音.理论分析和实验结果表明, PPBA算法相比较于同类算法能够满足高维数据发布安全性和可用性的个性化需求,同时具有更低的时间复杂度.

Personalized Differential Privacy Algorithm for High-Dimensional Data Publishing

In the process of privacy preserving high-dimensional data publishing, the size of the differential privacy budget directly affects the addition of noise. The privacy budget cannot be allocated reasonably for independent low-dimensional attribute sets, compromising the security and restricting availability of composite data sets. Then a Personalized Privacy Budget Allocation (PPBA) algorithm is proposed. The maximum support tree and weight values of attribute nodes are introduced to reduce the candidate space of attribute relationship pairs selected by the differential privacy index mechanism and enhance the accuracy of the Bayesian network. The dynamic weight values of nodes in the Bayesian network are set to rank the sensitivity of low-dimensional attribute sets. According to the personalized requirements for security and availability of published data sets, the constant allocation ratio q of differential privacy budgets is customized for the personalized allocation of Laplace noise to the low-dimensional attribute sets sorted by sensitivity. Theoretical analysis and experimental results reveal that the PPBA algorithm can meet the personalized requirements for security and availability of high-dimensional data publishing, with lower time complexity.