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Analyzing sensitive information leakage in trajectory embedding models

Published: 22 November 2022 Publication History

Abstract

With the proliferation of the mobile networks and location-based services, huge volume of user trajectories are collected to analyze the similarity among users and further unveil human mobility patterns for downstream tasks, such as point-of-interest recommendation and tourism planning. In recent works, trajectory embedding methods have been studied as efficient ways of trajectory similarity computation and effective inputs for downstream tasks, which embed trajectories into latent vector spaces equipped with the Euclidean distance to approximate the trajectory similarity and capture the characteristics of human mobility patterns. However, we demonstrate that such embedding, though hiding the locations, can leak the sensitive information of the trajectories, combined with auxiliary data. In this work, we propose trajectory embedding attack schemes to analyze the sensitive information leakage of the embedding vectors. In the experiment, we demonstrate that the passing areas, visited ROIs, and exact shapes of the trajectories are vulnerable under attacks on embedding vectors by the adversary with auxiliary information.

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Cited By

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  • (2024)Adversarial Reconstruction of Trajectories: Privacy Risks and Attack Models in Trajectory EmbeddingProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691274(259-269)Online publication date: 29-Oct-2024
  • (2024)Where Have You Been? A Study of Privacy Risk for Point-of-Interest RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671758(175-186)Online publication date: 25-Aug-2024
  • (2024)Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00261(2546-2555)Online publication date: 17-Jun-2024

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cover image ACM Conferences
SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
November 2022
806 pages
ISBN:9781450395298
DOI:10.1145/3557915
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 November 2022

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Author Tags

  1. privacy
  2. sensitive information leakage
  3. spatio-temporal embedding

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  • Research-article

Funding Sources

  • NSF China
  • Natural Science Foundation of Shanghai
  • Shanghai Sailing Program

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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View all
  • (2024)Adversarial Reconstruction of Trajectories: Privacy Risks and Attack Models in Trajectory EmbeddingProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691274(259-269)Online publication date: 29-Oct-2024
  • (2024)Where Have You Been? A Study of Privacy Risk for Point-of-Interest RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671758(175-186)Online publication date: 25-Aug-2024
  • (2024)Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00261(2546-2555)Online publication date: 17-Jun-2024

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