Contact
Universitätsstraße 38
70569 Stuttgart
Germany
Room: 2.356
Office Hours
on appointment
Subject
The digital revolution has led to significant technological advancements in the automotive industry, enabling vehicles to process and share information with other vehicles and the cloud. These data can be beneficial for achieving autonomous driving or creating safer road environments. However, they contain a significant amount of sensitive information that can be used to identify the underlying vehicle or construct a detailed profile of the driver. Thus, privacy concerns and issues are vital aspects of connected vehicle environments (CVEs).
Despite the drivers' desire to protect their sensitive data, their general demand is to continue utilizing as many connected vehicle-enabled applications (such as navigation or fatigue detection) as possible. Thus, a challenge of preserving privacy in CVEs is to balance the trade-off between privacy protection and service quality. Furthermore, research has shown that the sensitivity of personal data is related to when and where as well as for what purposes such information has been collected. This indicates that the privacy protection in CVEs must consider the dynamic and context-dependent nature of drivers' privacy needs. Lastly, to avoid the so-called "privacy paradox," where people claim to be concerned about their privacy but still share a lot of private information, user-friendly privacy management mechanisms should be developed.
The focus of my research is to develop a Situation-Aware Privacy-Preserving framework for Connected Vehicles that empowers drivers to manage their privacy preferences effectively while still harnessing the benefits of connected vehicle technology.
2024
Yunxuan Li, Christoph Stach and Bernhard Mitschang. "PaDS: An adaptive and privacy-enabling Data Pipeline for Smart Cars". 2024 25th IEEE International Conference on Mobile Data Management (MDM), Brussels, Belgium, 2024, pp. 41-50, doi: 10.1109/MDM61037.2024.00026.
Christoph Stach, Yunxuan Li, Laura Schuiki, and Bernhard Mitschang. "LALO—A Virtual Data Lake Zone for Composing Tailor-Made Data Products on Demand". In Database and Expert Systems Applications: 35th International Conference, DEXA 2024, Naples, Italy, August 26–28, 2024, Proceedings, Part II. Springer-Verlag, Berlin, Heidelberg, 288–305. doi: 10.1007/978-3-031-68312-1_22.
2023
Yunxuan Li, Pascal Hirmer and Christoph Stach, "CV-Priv: Towards a Context Model for Privacy Policy Creation for Connected Vehicles," 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Atlanta, GA, USA, 2023, pp. 583-588, doi: 10.1109/PerComWorkshops56833.2023.10150231.
Andrea Fieschi, Yunxuan Li, Pascal Hirmer, Christoph Stach and Bernhard Mitschang (2023). Privacy in Connected Vehicles: Perspectives of Drivers and Car Manufacturers. In: Aiello, M., Barzen, J., Dustdar, S., Leymann, F. (eds) Service-Oriented Computing. SummerSOC 2023. Communications in Computer and Information Science, vol 1847. Springer, Cham. doi: 10.1007/978-3-031-45728-9_4.
2022
Yunxuan Li, Pascal Hirmer, Christoph Stach, and Bernhard Mitschang. 2023. Ensuring Situation-Aware Privacy for Connected Vehicles. In Proceedings of the 12th International Conference on the Internet of Things (IoT '22). Association for Computing Machinery, New York, NY, USA, 135–138. doi: 10.1145/3567445.3569163.
PanDa: Adaptiver Datenschutz für Moderne Datenarchitekturen
BMBF Research project as part of Software Campus
Software-Defined Car
BMWi Research project
Summer Term 2025
- Supervision in Seminar "Vertiefungsthemen von Data Science"
- Orgnization of Pracitical Course
Summer Term 2024
- Supervision in Pracitical Course:
- Topic: Object Recognition with Common Picture PETs
Winter Term 23/24
- Supervision in Seminar "Data Management for End-to-End Machine Learning"
- Supervision in Pracitical Course:
- Topic: Extension of AdaPrivFlow
- Topic: Privacy Protection of Trajectory Data
Summer Term 2023
- Supervision in Seminar "Data Management for End-to-End Machine Learning"
- Supervision in Pracitical Course:
- Topic: Evaluation of Stream Processing Techniques
Winter Term 22/23
- Supervision in Pracitical Course:
- Topic: Privacy Context Model for Connected Vehicle Environments
Offered Student Projects
- Topic: Context Modeling for Improving Privacy-Friendly Interfaces
- In this project, students will explore context modeling for selected data privacy use cases and apply the results to extend and improve an existing graphical user interface. For further information, feel free to contact me.
Student Projects in Progress
Completed Student Projects
- Master Thesis:
- The Modeling of Privacy-Enhancing Technologies from the Privacy and Data Utility Aspect (Completed in March 2025)
- Bachelor Thesis:
- Interactive Selection of Privacy-Enhancing Technologies (Completed in March 2025)
- Master Thesis:
- Evaluation von Datenarchitekturen für die Connected-Cars-Domäne (Completed in June 2024)
- Research Project:
- Evaluating the Impact of Privacy-Enhancing Technologies on Autonomous Driving Functions (Completed in May 2024)
- Bachelor Thesis:
- Privacy-Enhancing Technology Repository for Connected Vehicles (Completed in November 2023)
- Master Thesis:
- Live Adaptation of Privacy-Enhancing Technologies in Connected Vehicles’ Data Pipelines (Completed in November 2023)
- Enpro:
- "SmartCar"-Datenplattform (Completed in May 2023)