Human mobility data is a crucial resource for urban mobility applications, such as city planning, traffic modeling, routing applications, or mobility services. Mobility data can bring valuable benefits, but it does not come without personal reference. The implementation of measures such as anonymization is thus needed to protect individuals' privacy. Naturally, a trade-off between privacy and utility arises as such techniques decrease the data’s utility which potentially limits its use.
This work aims to identify, explore implement and evaluate privacy-preserving techniques for mobility data and their impact on the usability in real-world use cases and datasets. Practitioners will likely only adopt such methods if these do not highly impair practical usage. Also, methods need to be made understandable and they need to be easy to implement by the users in practice. Even though large tech companies, such as Apple, Google, and Microsoft already make use of privacy methods with differential privacy guarantees, there is still a gap between state-of-the-art privacy methods and common practices within the majority of companies.
As the impact on applications’ utility stays unclear, practitioners hesitate to implement such methods. This calls for a set of comprehensible utility metrics that quantify the impact on the utility and make different methods easily comparable. Also, academic research often lacks usable implementations for its theoretical solutions that allow easy reuse of the proposed methods. Lacking resources are therefore another hurdle, as the implementation of complex privacy-preserving methods needs time and expertise.
With this work, I want to contribute to the practical applicability of suitable privacy methods for human mobility data according to state-of-the-art privacy research.