论文标题
具有连续非高斯属性的隐私保护对抗网络（PPAN）
Privacy-Preserving Adversarial Network (PPAN) for Continuous non-Gaussian Attributes
论文作者
论文摘要
最近提出了保护隐私的对抗网络（PPAN）作为信息理论框架，以解决数据共享中的隐私问题。该模型的主要思想是使用共同信息作为两个深神经网络的隐私措施和对抗训练，一种是机制，另一个作为对手。与分析上最佳的权衡相比，评估了PPAN模型用于离散合成数据，MNIST手写数字和连续高斯数据的性能。在这项研究中，我们评估了PPAN模型的连续非高斯数据，其中使用了保护隐私问题的下限和上限。这些边界包括基于k-th最近邻居的熵和共同信息的Kraskov（KSG）估计。除了综合数据集外，还检查了隐藏智能电表读数中实际用电量的实际情况。结果表明，对于连续的非高斯数据，PPAN模型在确定的最佳范围内执行并接近下限。
A privacy-preserving adversarial network (PPAN) was recently proposed as an information-theoretical framework to address the issue of privacy in data sharing. The main idea of this model was using mutual information as the privacy measure and adversarial training of two deep neural networks, one as the mechanism and another as the adversary. The performance of the PPAN model for the discrete synthetic data, MNIST handwritten digits, and continuous Gaussian data was evaluated compared to the analytically optimal trade-off. In this study, we evaluate the PPAN model for continuous non-Gaussian data where lower and upper bounds of the privacy-preserving problem are used. These bounds include the Kraskov (KSG) estimation of entropy and mutual information that is based on k-th nearest neighbor. In addition to the synthetic data sets, a practical case for hiding the actual electricity consumption from smart meter readings is examined. The results show that for continuous non-Gaussian data, the PPAN model performs within the determined optimal ranges and close to the lower bound.
