Machine learning as a service (MLaaS) has brought much convenience to our daily lives recently. However, the fact that the service is provided through cloud raises privacy leakage issues. In this work we propose the compressive privacy generative adversarial network (CPGAN), a data-driven adversarial learning framework for generating compressing representations that retain utility comparable to state-of-the-art, with the additional feature of defending against reconstruction attack. This is achieved by applying adversarial learning scheme to the design of compression network (privatizer), whose utility/privacy performances are evaluated by the utility classifier and the adversary reconstructor, respectively. Experimental results demonstrate that CPGAN achieves better utility/privacy trade-off in comparison with the previous work, and is applicable to real-world large datasets.

中文翻译：

---

压缩性隐私生成对抗网络

机器学习即服务（MLaaS）最近为我们的日常生活带来了很多便利。但是，通过云提供服务的事实引发了隐私泄漏问题。在这项工作中，我们提出了压缩性隐私生成对抗网络（CPGAN），它是一种数据驱动的对抗性学习框架，用于生成压缩表示形式，其保留了与最新技术相当的效用，并具有防御重建攻击的附加功能。这是通过将对抗性学习方案应用于压缩网络（私有化器）的设计来实现的，压缩网络的效用/隐私性能分别由效用分类器和对手重建器评估。实验结果表明，与以前的工作相比，CPGAN在效用/隐私权方面取得了更好的折衷，