A Digital Twin-based Privacy Enhancement Mechanism for the Automotive Industry

Abstract

This paper discusses a Digital Twin demonstrator for privacy enhancement in the automotive industry. Here, the Digital Twin demonstrator is presented as a method for the design and implementation of privacy enhancement mechanisms, and is used to detect privacy concerns and minimize breaches and associated risks to which smart car drivers can be exposed through connected infotainment applications and services. The Digital Twin-based privacy enhancement demonstrator is designed to simulate variety of conditions that can occur in the smart car ecosystem. We firstly identify the core stakeholders (actors) in the smart car ecosystem, their roles and exposure to privacy vulnerabilities and associated risks. Secondly, we identify assets that consume and generate sensitive privacy data in smart cars, their functionalities, and relevant privacy concerns and risks. Thirdly, we design an infrastructure for collecting (i) real-time sensor data from smart cars and their assets, and (ii) environmental data, road and traffic data, generated through operational driving lifecycle. In order to ensure compliance of the collected data with privacy policies and regulations, e.g. with GDPR requirements for enforcement of the data subject’s rights, we design methods for the Digital Twin-based privacy enhancement demonstrator that are based on behavioural analytics informed by GDPR. We also perform data anonymization to minimize privacy risks and enable actions such as sending an automatic informed consent to the stakeholders.