Privacy Preserving Services for Intelligent Transportation Systems with Homomorphic Encryption

Abstract

With the advent of intelligent transportation systems, vehicles will connect continuously to the Internet via the vehicular core network or the cellular network. Opening vehicles systems to the Internet aims at improving vehicles safety and comfort via the development of remote services for drivers assistance. Such services are for example infotainment applications, software update over the air, remote diagnostics and adaptive insurance. However, some of these services come with an inherent problem of privacy as they require as inputs the private data from the vehicles. In this work, we investigate the use of homomorphic encryption for ensuring the confidentiality of vehicles private data. We study the confidentiality of data, which are treated by external service providers such as cars manufacturers, their stakeholders and insurances. Our protocol ensures, by design, the private treatment of vehicles data thanks to homomorphic encryption properties. We validate our proposal by studying drivers behaviour using a simple neural network that takes as input drivers pictures and tells whether a driver is concentrated or distracted. Indeed, we rely on a 3 layers network for classifying drivers behavior in 10 different classes from normal to dangerous. We use a quadratic activation function for intermediate layers which contain 20 and 10 units, respectively. Meanwhile, we use a sigmoid activation function for the last layer which contains 10 units, one per label. Our classification takes 11 seconds with a classification accuracy of 86% and 25 seconds with a classification accuracy of 92%.