Towards an Identity-Based Data Model for an Automotive Privacy Process

Abstract

Information technology has attracted considerable attention in modern automobiles for their promise of value-added services. Based on increasing connectivity and seamless integration of advanced functionality into vehicles, a new challenge is the development of holistic and standardized privacy approaches. So far, privacy has often been considered as a singular task, neglecting the impact of a holistic viewpoint on automotive data. In this paper we provide an identity-based data model, a way to define a structured and flexible view to the acquired vehicular data, i.e., identifying information. We develop the data model as a graph, provide a formal notation and demonstrate its application with an example. The proposed scheme of the model is of multiple uses and the formal notation shows to serve additional privacy features to our model, e.g., privacy risk assessment.