(PrivBayes)构建基于差分隐私的贝叶斯网络数据发布模型
建立基于差分隐私的贝叶斯网络，使得结构化数据同时兼备隐私性与效用性
1）对原始d维数据集预处理：运用二分k均值算法对连续型数据离散化 2）设置差分隐私预算epsilon1，使用指数机制来构造k度的贝叶斯网络N，并输出d对AP的概率分布 3）设置差分隐私运算epsilon2，在d对AP对的概率分布中加入拉普拉斯噪声 4）生成具有噪声的近似AP对概率分布 5）从具有噪声的概率分布中采样，生成发布数据集 6）通过α-边际分布和SVM分类器评估新生成的数据集的隐私性和效用性。
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