Internet of Things (IoT) and Cloud Computing (CC) technologies are becoming critical requirements to the advancement of intelligent transportation systems (ITSs). ITSs generally rely on captured images to evaluate the status of traffic and perform vehicle statistics. However, such images may contain confidential information, and thus, securing such images is paramount. Therefore, we propose in this paper an efficient framework for improving the security of CC-IoT based ITSs. The proposed framework allows extracting particular vehicle information without revealing any sensitive information. Towards this goal, a convolutional neural network is used to classify encrypted images, based on the vehicle type in real-time, obtained by cameras integrated into road-side units that are part of an ITS leaving sensitive information in all images hidden. Within the proposed framework, we develop a new image classification architecture that never fully decrypts the captured images, thus protecting drivers’ personal information, such as location, license plate, and vehicle contents. In addition, the system does not require a fully decrypted image, which increases the system computational efficiency as compared to conventional systems. The obtained results show that the proposed partial decryption classification technique presents up to 18% reduction in average computational complexity when compared with a fully decrypted system.

中文翻译：

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基于云的ITS中卷积神经网络加密图像分类框架

物联网（IoT）和云计算（CC）技术正成为智能交通系统（ITS）进步的关键要求。ITS通常依靠捕获的图像来评估交通状况并执行车辆统计。但是，此类图像可能包含机密信息，因此，保护​​此类图像至关重要。因此，我们在本文中提出了一个有效的框架来提高基于CC-IoT的ITS的安全性。所提出的框架允许提取特定的车辆信息而不泄露任何敏感信息。为了实现这一目标，使用卷积神经网络基于车辆类型实时地对加密图像进行分类，这些图像是由集成到作为ITS一部分的路边单元中的摄像头获得的，从而隐藏了所有图像中的敏感信息。在提议的框架内，我们开发了一种新的图像分类体系结构，该体系结构永远不会完全解密捕获的图像，从而保护驾驶员的个人信息，例如位置，车牌和车辆内容。另外，该系统不需要完全解密的图像，与传统系统相比，这提高了系统的计算效率。获得的结果表明，与完全解密的系统相比，提出的部分解密分类技术的平均计算复杂度降低了18％。该系统不需要完全解密的图像，与传统系统相比，可以提高系统的计算效率。获得的结果表明，与完全解密的系统相比，提出的部分解密分类技术的平均计算复杂度降低了18％。该系统不需要完全解密的图像，与传统系统相比，可以提高系统的计算效率。获得的结果表明，与完全解密的系统相比，提出的部分解密分类技术的平均计算复杂度降低了18％。