Heterogeneous and Customized Cost-Efficient Reversible Image Degradation for Green IoT

With the large-scale deployment of the Internet of Things (IoT) in daily life, more and more privacy data are collected by IoT devices. These data are not directly physically controlled by users, which may cause privacy concerns. In fact, privacy has become one of the significant problems faced by IoT. In this article, we mainly study the protection of image privacy under the green IoT. We have conducted an in-depth analysis of the green IoT scenario and put forward the scope and corresponding goals that the scheme should have. Motivated by this, a novel image privacy protection scheme is proposed, i.e., heterogeneous and customized cost-efficient reversible image degradation for green IoT. This scheme fully considers the characteristics of privacy and the various users' diverse requirements to achieve a heterogeneous and customized privacy protection. Meanwhile, cost effectiveness cannot be confined to the efficiency of the direct image processing at the expense of greatly increasing costs in other aspects, such as transmission and reversion. It is mitigated by preserving some visual content in the privacy-protected image. It also improves the image compression efficiency and ensures that the user can select the desired image according to the visual content for reversion. Some experiments have been carried out to demonstrate that this work has achieved the proposed scope and corresponding goals.

Automated summary

With the widespread use of the Internet of Things (IoT), privacy concerns have emerged as a significant issue due to the collection of user data by IoT devices. This article focuses on the protection of image privacy in the context of green IoT. A novel scheme called heterogeneous and customized cost-efficient reversible image degradation is proposed, which takes into consideration privacy characteristics and diverse user requirements. The scheme aims to achieve personalized privacy protection while maintaining cost effectiveness by preserving visual content in privacy-protected images and improving image compression efficiency.