freemove
Transdisciplinary Research on Privacy-Centered Mobility Data for Sustainable Urban Transformation
freemove is a transdisciplinary project on mobility data research funded by the German Federal Ministry of Education and Research (BMBF). The research group combines the strengths of academic and practice-oriented partners from the fields of machine learning, digital self-determination, human-centered computing and information security.
Note that not all parts of this website are available in English at the moment.
Project Goal
The goal of the project is to develop a scientifically based, holistic framework that specifies the requirements for a fair, useful, secure provision of mobility data for public and private users.
Status Quo & Research Questions
The potential of analyzing movement data is enormous, be it for addressing critical problems such as epidemics and disasters, or also for sustainable, human-centered, and environmentally conscious urban planning and transportation. At the same time, there are challenges associated with making such movement data available: the high level of protection of individuals' privacy required both legally and ethically demands sophisticated mathematical and technical anonymization procedures.
The usability of the data, for example for statistical and algorithmic modeling (usability) on the one hand and the need for data protection and data security on the other hand are conflicting goals. To enable trade-offs, a number of questions must first be answered:
How can the risk of deanonymization, with respect to the data and the context in which it is generated and used, be reliably estimated and assessed?
How can technical procedures for anonymizing data be explained and communicated to users?
How can the ideas and values of citizens who make their data available be taken into account in the process of releasing and making it available to third parties?
New research and development projects can be supported by the privacy-centered collection of mobility behavior, which will be developed in this transdisciplinary project and tested in field studies.
Publications
- Valentin Rupp, Max von Grafenstein: Clarifying “Personal Data” and the Role of Anonymisation in Data Protection Law: Including and Excluding Data from the Scope of the GDPR (more clearly) through Refining the Concept of Data Protection, Computer Law and Security Review 52, DOI: 10.1016/j.clsr.2023.105932.
- Alexandra Kapp and Helena Mihaljevic (2023): Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios, Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL '23). Association for Computing Machinery, Article 93, 1–12. DOI: 10.1145/3589132.3625661
- Peter Sörries, Daniel Franzen, Markus Sperl, Claudia Müller-Birn (2023): "Foregrounding Values through Public Participation: Eliciting Values of Citizens in the Context of Mobility Data Donation, MuC '23: Proceedings of Mensch und Computer 2023. DOI: 10.1145/3603555.3608531.
- Alexandra Kapp, Julia Hansmeyer, Helena Mihaljević (2023): "Generative Models for Synthetic Urban Mobility Data: A Systematic Literature Review, ACM Computing Surveys, DOI: 10.1145/3610224.
- Daniel Franzen, Saskia Nuñez von Voigt, Peter Söres, Florian Tschorsch, Claudia Müller-Birn (2022): "Am I Private and If So, how Many?" - Using Risk Communication Formats for Making Differential Privacy Understandable, ACM Conference on Computer and Communications Security (CCS), DOI: 10.48550/arXiv.2204.04061.
- Alexandra Kapp, Saskia Nuñez von Voigt, Helena Mihaljević & Florian Tschorsch (2022): Towards mobility reports with user-level privacy, Journal of Location Based Services, DOI: 10.1080/17489725.2022.2148008.
- Alexandra Kapp (2022): Collection, usage and privacy of mobility data in the enterprise and public administrations, Proceedings on Privacy Enhancing Technologies.
- Luise Mehner, Saskia Nuñez von Voigt, Florian Tschorsch (2021): Towards Explaining Epsilon: A Worst-Case Study of Differential Privacy Risks. EuroS&P Workshops 2021: 328-331.