The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public. Even though differential privacy (DP) is a widely accepted criterion that can provide a provable privacy guarantee, the application of DP on unstructured data such as images is not trivial due to the lack of a clear qualification on the meaningful difference between any two images. In this paper, for the first time, we introduce a novel notion of image-aware differential privacy, referred to as DP-image, that can protect user's personal information in images, from both human and AI adversaries. The DP-Image definition is formulated as an extended version of traditional differential privacy, considering the distance measurements between feature space vectors of images. Then we propose a mechanism to achieve DP-Image by adding noise to an image feature vector. Finally, we conduct experiments with a case study on face image privacy. Our results show that the proposed DP-Image method provides excellent DP protection on images, with a controllable distortion to faces.
翻译:在社交网络、政府数据库和工业应用中过度使用图像带来了巨大的隐私风险,引起了公众的严重关切。尽管差异隐私(DP)是一个得到广泛接受的标准,可以提供可验证的隐私保障,但是,在图像等非结构化数据上应用DP并不是微不足道的,因为对于任何两种图像之间的有意义的差别缺乏明确的限定。在本文件中,我们首次引入了图像认知差异隐私的新概念,称为DP图像,可以保护用户在图像上的个人信息,不受人类和AI对手的伤害。DP-Imaage定义是传统差异隐私的扩大版本,考虑到图像的地貌空间矢量之间的距离测量。然后我们提出一个机制,通过在图像特征矢量上添加噪音来实现DP-IMage。最后,我们用一个案例研究对面图像隐私进行实验。我们的结果显示,拟议的DP-Imaage方法在图像上提供了极好的DP-image保护,并具有可控制的面扭曲性。