LAUSR

Detection of vehicular congestion has a significant effect on the transportation sector. Several research approaches have been proposed to improve accuracy and efficiency with the advent of the Internet of Vehicles (IoV). Nonetheless, the privacy issue has attracted little attention on congestion detection. In this paper, we put forward a privacy-preserving and efficient scheme for vehicular congestion detection with both horizontal and vertical partitioned vehicular sensing data using Multilayer Perceptron (MLP) approach. Particularly, this work considers that different vehicle manufacturers would choose their encryption systems, which poses a challenge on how to collaboratively train an MLP model on these heterogeneous encrypted sensing data. To address this challenge, we utilize proxy re-encryption and additive homomorphic encryption schemes for preserving the confidentiality of raw sensing data and learning models while maintaining secure computation efficiency. Formally security analysis is provided to demonstrate that our proposed scheme is able to guarantee the privacy of the MLP learning process. Finally, we implement our scheme and evaluate it with real datasets. Experimental results show the effectiveness and feasibility of our MLP learning under multiple encryption systems for vehicular congestion detection.