Vortrag von Johannes Gasthuber: „Anonymizing trajectory data without accuracy loss (by suppression)“

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Am 21. Juli 2022 um 15 Uhr hält Johannes Gasthuber einen Vortrag zum Thema „Anonymizing trajectory data without accuracy loss (by suppression)“.

Zur Teilnahme via Zoom verwenden Sie die folgenden Daten:


Meeting-ID: 615 6723 8876

Kenncode: 126009

Unten stehend finden Sie die Kurzfassung des Vortrags in englischer Sprache.


Tracking human mobility becomes more common and leads to overall improvements for individuals and society. Both companies and governments try to get insights from the data to provide better services and understand the needs of the individual. Navigation services and better city planning are just some examples. But a company can not just publish user data since it is private. This thesis explores why mobility data is sensitive personal data and how attackers try to learn from movement data to breach an individual’s privacy. The thesis proposes an algorithm for a real-world use case: publishing bicycle movement data to the city council. Since other approaches do not achieve the necessary accuracy of data, the thesis proposes a new anonymization algorithm based on combining filtering a user’s private locations and hiding him in a group. The proposed anonymization algorithm is evaluated with a small dataset and achieves its privacy definition. Additionally, the thesis proposes how to continue the research once more data is available.