Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 31 |
| Fachzeitschrift | ACM Transactions on Spatial Algorithms and Systems |
| Jahrgang | 10 |
| Ausgabenummer | 4 |
| Frühes Online-Datum | 5 Feb. 2024 |
| Publikationsstatus | Veröffentlicht - 23 Okt. 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Signalverarbeitung
- Informatik (insg.)
- Information systems
- Mathematik (insg.)
- Modellierung und Simulation
- Informatik (insg.)
- Angewandte Informatik
- Mathematik (insg.)
- Geometrie und Topologie
- Mathematik (insg.)
- Diskrete Mathematik und Kombinatorik
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in: ACM Transactions on Spatial Algorithms and Systems, Jahrgang 10, Nr. 4, 31, 23.10.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › 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 -