research-article

Reconstruction Attack on Differential Private Trajectory Protection Mechanisms

Authors:

Alsharif Abuadbba,

Salil Subhash KanhereAuthors Info & Claims

ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

Pages 279 - 292

https://doi.org/10.1145/3564625.3564628

Published: 05 December 2022 Publication History

Abstract

Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the potential to reveal sensitive information about individuals, e.g., religious beliefs or sexual orientations. Accordingly, trajectory datasets require appropriate sanitization. Due to their strong theoretical privacy guarantees, differential private publication mechanisms receive much attention. However, the large amount of noise required to achieve differential privacy yields structural differences, e.g., ship trajectories passing over land. We propose a deep learning-based Reconstruction Attack on Protected Trajectories (RAoPT), that leverages the mentioned differences to partly reconstruct the original trajectory from a differential private release. The evaluation shows that our RAoPT model can reduce the Euclidean and Hausdorff distances between the released and original trajectories by over 68 % on two real-world datasets under protection with ε ≤ 1. In this setting, the attack increases the average Jaccard index of the trajectories’ convex hulls, representing a user’s activity space, by over 180 %. Trained on the GeoLife dataset, the model still reduces the Euclidean and Hausdorff distances by over 60 % for T-Drive trajectories protected with a state-of-the-art mechanism (ε = 0.1). This work highlights shortcomings of current trajectory publication mechanisms, and thus motivates further research on privacy-preserving publication schemes.

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Cited By

D'Silva NShahi TDokk Husveg ØSanjeeve ABuchholz EKanhere S(2024)Demystifying Trajectory Recovery from Ash: An Open-Source Evaluation and Enhancement2024 17th International Conference on Security of Information and Networks (SIN)10.1109/SIN63213.2024.10871881(1-8)Online publication date: 2-Dec-2024
https://doi.org/10.1109/SIN63213.2024.10871881
Merhi JBuchholz EKanhere S(2024)Synthetic Trajectory Generation Through Convolutional Neural Networks2024 21st Annual International Conference on Privacy, Security and Trust (PST)10.1109/PST62714.2024.10788061(1-12)Online publication date: 28-Aug-2024
https://doi.org/10.1109/PST62714.2024.10788061
Bouabba SZeitouni KHaidar BAgoulmine NDagdia Z(2024)Federated TimeGAN for Privacy Preserving Synthetic Trajectory Generation2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00062(301-306)Online publication date: 24-Jun-2024
https://doi.org/10.1109/MDM61037.2024.00062
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Index Terms

Reconstruction Attack on Differential Private Trajectory Protection Mechanisms
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Database and storage security
    1. Data anonymization and sanitization
  2. Human and societal aspects of security and privacy
    1. Privacy protections

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ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

December 2022

1021 pages

ISBN:9781450397599

DOI:10.1145/3564625

Copyright © 2022 ACM.

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Published: 05 December 2022

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Cited By

D'Silva NShahi TDokk Husveg ØSanjeeve ABuchholz EKanhere S(2024)Demystifying Trajectory Recovery from Ash: An Open-Source Evaluation and Enhancement2024 17th International Conference on Security of Information and Networks (SIN)10.1109/SIN63213.2024.10871881(1-8)Online publication date: 2-Dec-2024
https://doi.org/10.1109/SIN63213.2024.10871881
Merhi JBuchholz EKanhere S(2024)Synthetic Trajectory Generation Through Convolutional Neural Networks2024 21st Annual International Conference on Privacy, Security and Trust (PST)10.1109/PST62714.2024.10788061(1-12)Online publication date: 28-Aug-2024
https://doi.org/10.1109/PST62714.2024.10788061
Bouabba SZeitouni KHaidar BAgoulmine NDagdia Z(2024)Federated TimeGAN for Privacy Preserving Synthetic Trajectory Generation2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00062(301-306)Online publication date: 24-Jun-2024
https://doi.org/10.1109/MDM61037.2024.00062
Kim JJang B(2024)Privacy-preserving generation and publication of synthetic trajectory microdata: A comprehensive surveyJournal of Network and Computer Applications10.1016/j.jnca.2024.103951230(103951)Online publication date: Oct-2024
https://doi.org/10.1016/j.jnca.2024.103951
Miranda-Pascual ÀGuerra-Balboa PParra-Arnau JForné JStrufe T(2024)An overview of proposals towards the privacy-preserving publication of trajectory dataInternational Journal of Information Security10.1007/s10207-024-00894-023:6(3711-3747)Online publication date: 4-Sep-2024
https://doi.org/10.1007/s10207-024-00894-0
Hu Z(2023)Research on differential privacy protection method based on user tendencyPLOS ONE10.1371/journal.pone.028882318:10(e0288823)Online publication date: 26-Oct-2023
https://doi.org/10.1371/journal.pone.0288823
Ozeki RYonekura HRizk HYamaguchi H(2023)Balancing Privacy and Utility of Spatio-Temporal Data for Taxi-Demand Prediction2023 24th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM58254.2023.00044(215-220)Online publication date: Jul-2023
https://doi.org/10.1109/MDM58254.2023.00044
Yonekura HOzeki RRizk HYamaguchi H(2023)DEMO: STM - A Privacy-Enhanced Solution for Spatio-Temporal Trajectory Management2023 24th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM58254.2023.00034(168-171)Online publication date: Jul-2023
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