There is a strong positive correlation between the development of deep learning and the amount of public data available. Not all data can be released in their raw form because of the risk to the privacy of the related individuals. The main objective of privacy-preserving data publication is to anonymize the data while maintaining their utility. In this paper, we propose a privacy-preserving semi-generative adversarial network (PPSGAN) that selectively adds noise to class-independent features of each image to enable the processed image to maintain its original class label. Our experiments on training classifiers with synthetic datasets anonymized with various methods confirm that PPSGAN shows better utility than other conventional methods, including blurring, noise-adding, filtering, and generation using GANs.

中文翻译：

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用于保护隐私的图像数据发布的选择性特征匿名化

深度学习的发展与可用公共数据量之间存在很强的正相关关系。由于存在相关个人隐私的风险，因此并非所有数据都可以原始格式发布。隐私保护数据发布的主要目的是在保持数据实用性的同时对数据进行匿名处理。在本文中，我们提出了一种隐私保护的半生成对抗网络（PPSGAN），该网络有选择地向每个图像的类无关功能添加噪声，以使处理后的图像能够保持其原始类标签。我们在训练分类器上使用各种方法匿名化的合成数据集的实验证实，PPSGAN显示出比其他常规方法更好的效用，包括模糊，噪声添加，滤波和使用GAN生成。