Convolutional neural network framework for encrypted image classification in cloud-based ITS

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.