A Privacy-Preserving Infrastructure for Driver's Reputation Aware Automotive Services

摘要

Even though the introduction of ICT in transportation systems leads to several advantages in terms of efficiency of transport, mobility, traffic management, and in improved interfaces between different transport modes, it also brings some drawbacks in terms of increasing security challenges, also related to human behavior. For this reason, in the last decades, attempts to characterize drivers' behavior have been mostly targeted towards risk assessment and, more recently, to the training of machine learning software for autonomous driving. In this paper, we propose, for the first time, to use driver behavioral characterization to build a general reputation profile, that can be used to create innovative, reputation-aware automotive services. As a first step towards realizing this vision, we present guidelines for the design of a privacy preserving vehicular infrastructure that is capable of collecting information generated from vehicles sensors and the environment, and to compose the collected information into driver reputation profiles. In turn, these profiles are exchanged in a privacy preserving way within the infrastructure to realize reputation-aware automotive services, a sample of which are described in the paper. As a fundamental component of the infrastructure, we show that: ⅰ) multi-dimensional reputation profiles can be formed building upon the recently introduced notion of driver DNA; ⅱ) multi-dimensional comparison of profiles can be achieved by means of a reputation lattice rooted in the notion of algebraic c-semiring; and ⅲ) a secure two-party mechanism can used to provide services to drivers on the basis of their reputation and/or DNA's parameters.