Details
Original language | English |
---|---|
Article number | 31 |
Journal | ACM Transactions on Spatial Algorithms and Systems |
Volume | 10 |
Issue number | 4 |
Early online date | 5 Feb 2024 |
Publication status | Published - 23 Oct 2024 |
Abstract
GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely, the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace - a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory's origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all datasets and identifies modified GPS trajectories effectively and efficiently.
Keywords
- contrastive learning, data privacy, GPS trajectories, personal data, spatio-temporal data
ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
- Computer Science(all)
- Information Systems
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Geometry and Topology
- Mathematics(all)
- Discrete Mathematics and Combinatorics
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In: ACM Transactions on Spatial Algorithms and Systems, Vol. 10, No. 4, 31, 23.10.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - RE-Trace
T2 - Re-identification of Modified GPS Trajectories
AU - Schestakov, Stefan
AU - Gottschalk, Simon
AU - Funke, Thorben
AU - Demidova, Elena
N1 - Publisher Copyright: © 2024 Copyright held by the owner/author(s).
PY - 2024/10/23
Y1 - 2024/10/23
N2 - GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely, the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace - a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory's origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all datasets and identifies modified GPS trajectories effectively and efficiently.
AB - GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely, the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace - a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory's origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all datasets and identifies modified GPS trajectories effectively and efficiently.
KW - contrastive learning
KW - data privacy
KW - GPS trajectories
KW - personal data
KW - spatio-temporal data
UR - http://www.scopus.com/inward/record.url?scp=85203093865&partnerID=8YFLogxK
U2 - 10.1145/3643680
DO - 10.1145/3643680
M3 - Article
AN - SCOPUS:85203093865
VL - 10
JO - ACM Transactions on Spatial Algorithms and Systems
JF - ACM Transactions on Spatial Algorithms and Systems
SN - 2374-0353
IS - 4
M1 - 31
ER -