Master Thesis MSTR-2023-89

BibliographySinger, Patrick: Enhancing privacy in car data : anonymization techniques and metrics evaluation.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 89 (2023).
96 pages, english.

With the advancement of connected vehicles in the automotive domain, data collection and sharing between vehicles or between vehicles and manufacturers reaches considerable proportions. The excellent possibilities of utilizing this data for product improvement are restrained by the fact that vehicle data contains personal information about drivers. Privacy-preserving technologies have been subject to research in various fields, but not as much in the automotive domain. Consequently, metrics evaluating the privacy and data quality provided by these technologies also remain scarce in this field. In this work, we apply different anonymization approaches to real-world vehicle data. We assess the performance of the approaches using a selection of metrics from the literature. Additionally, two domain-specific demonstrators are designed and implemented to analyze the privacy and data utility the approaches provide. The results show that privacy protection for vehicle data poses new challenges. We motivate the introduction of domain-specific metrics to evaluate the privacy and data quality of anonymization approaches in a useful way.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems
Superviser(s)Mitschang, Prof. Bernhard; Fieschi, Andrea; Hirmer, Dr. Pascal
Entry dateFebruary 20, 2024
   Publ. Computer Science