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Adversarial Reconstruction of Trajectories: Privacy Risks and Attack Models in Trajectory Embedding

Published: 22 November 2024 Publication History

Abstract

Human trajectories, representing sequences of location points over time, are extensively collected and analyzed for various real-world applications such as urban planning, transportation management, and personalized location-based services. Trajectory embedding transforms raw trajectories into vector representations, capturing the underlying patterns and structures in the data. However, the abstraction provided by vector representations introduces significant security and privacy risks. These embeddings, often shared between entities or organizations, can be exploited by adversaries to reconstruct original trajectories, thereby compromising individual privacy. In this paper, we investigate the privacy issues of trajectory embeddings from an adversary's perspective. We propose two types of attacks to reconstruct original trajectories using road network information, addressing scenarios where the adversary has varying degrees of access to the black-box representation model. The first attack assumes unrestricted access to the model, allowing the adversary to construct a large-scale dataset and train a neural network to predict the road sequence of the trajectories. The second attack considers limited access, where the adversary computes distance coordinates between selected trajectory landmarks and road segments to infer different parts of the trajectory. Our experiments on a real-world dataset demonstrate that the reconstructed trajectories outperform baseline methods, achieving substantially lower reconstruction errors and more accurate alignment with the original trajectories, highlighting the significant vulnerability of trajectory embeddings to privacy breaches. These findings underscore the need for robust privacy-preserving mechanisms in spatio-temporal data analysis.

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      cover image ACM Conferences
      SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
      October 2024
      743 pages
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      Published: 22 November 2024

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      Author Tags

      1. location privacy
      2. reconstruction attack
      3. trajectory embedding

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