School of Space Information, Space Engineering University, Beijing 101416, China.
School of Computer Science, University of Technology Sydney, Sydney, NSW 2007, Australia.
Sensors (Basel). 2020 Dec 24;21(1):58. doi: 10.3390/s21010058.
With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users' privacy while maintaining image utility.
随着多媒体物联网（IoMT）的发展，越来越多的图像数据由各种多媒体设备收集，如智能手机、相机和无人机。这些海量的图像广泛应用于 IoMT 的各个领域，这给隐私保护带来了巨大的挑战。在本文中，我们提出了一个新的图像隐私保护框架，以保护 IoMT 设备收集的图像中包含的敏感个人信息。我们旨在使用深度神经网络技术识别图像中的隐私敏感内容，然后使用具有差分隐私（DP）的生成对抗网络（GAN）生成的合成内容来保护它。我们的实验结果表明，所提出的框架可以在保持图像实用性的同时，有效地保护用户的隐私。
