The Long Trajectory to Trajectory Privacy
The Long Trajectory to Trajectory Privacy
Our movements disclose a wealth of sensitive information about us - from our secret habits to religious and political opinions. Yet, legitimate usage of trajectory data can benefit the entire society, as highlighted by contact tracing during the recent pandemic. Therefore, numerous approaches for protecting the privacy of trajectory data have been proposed. Even today's de facto privacy standard, differential privacy (DP), has been applied to the trajectory domain. Still, recent works [2] show that we are still far from our goal of releasing high-quality trajectories for arbitrary applications under strong guarantees.
This talk will provide a gentle introduction to the field of location trajectories and their privacy protection. We will begin the journey with the well-known notion of Geo-Indistinguishability for location privacy and discuss why protecting locations is not enough. After a brief overview of protection mechanisms, especially those providing DP, and their shortcomings, we look into a recent attack [1] that uses deep learning to attack differential privacy - or does it? Finally, we explore the connected but distinct idea of generating synthetic yet realistic trajectories instead of adding protection to the real data. Could this be the solution?
Based on the following works:
[1] Erik Buchholz, Alsharif Abuadbba, Shuo Wang, Surya Nepal, and Salil S. Kanhere. Reconstruction Attack on Differential Private Trajectory Protection Mechanisms. In Proceedings of the 38th Annual Computer Security Applications Conference (ACSAC ’22), 12 2022.
[2] Erik Buchholz, Alsharif Abuadbba, Shuo Wang, Surya Nepal, and Salil S. Kanhere. SoK: Can Trajectory Generation Combine Privacy and Utility?. Proceedings on Privacy Enhancing Technologies, 2024(3), July 2024.
Click the link below to join the seminar at the following time
Melbourne (AEDT) : 4 Jun 2024 11:00 AM
Japan (JST) : 4 Jun 2024 9:00 AM
China (CST) : 4 Jun 2024 8:00 AM
India (IST) : 4 Jun 2024 5:30 AM
Central Europe (CEST) : 4 Jun 2024 2:00 AM
New York (EDT) : 3 Jun 2024 8:00 PM
Los Angeles (PDT) : 3 Jun 2024 5:00 PM
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Webinar passcode: 998805 (if asked when joining the seminar)
About the speaker
PhD Candidate, UNSW Sydney
Erik Buchholz is a PhD candidate at UNSW Sydney, specialising in privacy-enhancing technologies. His work, supported by the Cyber Security Cooperative Research Centre (CSCRC) of Australia and UNSW, aims to publish location trajectories without compromising privacy. Previously, he completed Bachelor's and Master's degrees with distinction at RWTH Aachen, Germany, and received two awards for his Master's thesis on a privacy-preserving exchange platform for the injection moulding industry. His PhD's first two projects regarding a reconstruction attack on differentially private trajectory protection and a systematisation of knowledge on generative trajectory models have been published at the top conferences ACSAC and PETS.
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