Controlling and filtering users data in Intelligent Transportation System

Abstract Intelligent Transportation Systems (ITS) provide mechanisms so that users are better informed in order to use efficiently the existing and future transportation capabilities. However, in order for ITS to be helpful and reliable in real-life situations, security and privacy considerations have to be taken into account. Users are contributing with data captured from private sensors, raising privacy risks when sharing this within the ITS context. For example, GPS data can facilitate the construction of ITS services for route discovery, but in the same time malicious users can use the information in order to derive location patterns and geographical habits. Several ITS companies could gain interesting insights about the traffic and safety events, if they put together owned private data. However, at this moment, the security risks prohibit such an endeavor. In this paper, we illustrate a security model where ITS participants can specify how data sharing captured by an ITS application will behave in regards to their own privacy requirements. The proposed solution is able to mediate the differences between ITS applications needs regarding data usage under various context based constraints and user focused constraints defined using security policies for their shared data. The next topic discussed in the paper is our proposal of an appropriate ITS mechanism that manages to establish the level of trust in the information disseminated in the system. The trust level mechanism is used in order to decide whether an ITS event should be are advertised or not to other users or ITS applications. In systems like ITS that are dynamic and changing frequently, trust in shared data must be calculated taking into consideration both the contextual information disseminated in the system, but also the sources’ reputation. Our proposals’ evaluations is done using ITS implementations in real-world conditions.