Communications of the ACM
Virtual trip lines for distributed privacy-preserving traffic monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Trajectory anonymity in publishing personal mobility data
ACM SIGKDD Explorations Newsletter
Sketch-based querying of distributed sliding-window data streams
Proceedings of the VLDB Endowment
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The organization and planning of services (e.g. shopping facilities, infrastructure) requires quantitative information about the number of customers and their frequency of visiting. In this paper we present a framework which enables the collection of quantitative visit information for arbitrary sets of locations in a distributed and privacy-preserving way. While trajectory analysis is typically performed on a central database requiring the transmission of sensitive personal movement information, the main principle of our approach is the local processing of movement data. Only aggregated statistics are transmitted anonymously to a central coordinator, which generates the global statistics. In this paper we present our approach including the methodical background that enables distributed data processing as well as the architecture of the framework.