标题： 差分隐私合成混合数据生成用于无监督学习 显示英文标题

标题： Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning

摘要： 我们引入了DP-auto-GAN框架用于合成数据生成，该框架结合了自动编码器的低维表示和生成对抗网络（GANs）的灵活性。 此框架可以用来接收原始敏感数据，并在保护隐私的情况下训练模型以生成具有与原始数据相似统计特性的合成数据。 通过差分隐私的后处理保证，这个学习到的模型可以生成任意数量的合成数据，然后可以自由共享。 我们的框架适用于未标记的混合类型数据，可能包括二进制、分类和实数值数据。 我们在二进制数据（MIMIC-III）和混合类型数据（ADULT）上实现了这个框架，并在无监督设置下的指标上将其性能与现有的私有算法进行了比较。 我们还介绍了一种新的定量度量方法，能够检测合成数据的多样性或缺乏多样性。 显示英文摘要

摘要： We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that will satisfy similar statistical properties as the original data. This learned model can generate an arbitrary amount of synthetic data, which can then be freely shared due to the post-processing guarantee of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both binary data (MIMIC-III) and mixed-type data (ADULT), and compare its performance with existing private algorithms on metrics in unsupervised settings. We also introduce a new quantitative metric able to detect diversity, or lack thereof, of synthetic data.