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Trajectory Data Collection with Local Differential Privacy

Summary: Proposes a pure ε-LDP trajectory perturbation mechanism that uniquely leverages adjacent-direction information of each point to constrain perturbation candidates using only the underlying location set. Adds an anchor-based adaptive region restriction to tighten perturbed trajectories without external priors, markedly improving utility in extensive real and synthetic experiments. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13293
Venue
VLDB
Year
2023
Pagerank
5.093636e-05
Overall Rank
11,444 | 21.49%
DOI
10.14778/3603581.3603597

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BibTeX Citation

@article{zhang_vldb23,
        title = {{Trajectory Data Collection with Local Differential Privacy}},
        author = {Zhang, Yuemin and Ye, Qingqing and Chen, Rui and Hu, Haibo and Han, Qilong},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {10},
        pages = {2591--2604},
        doi = {10.14778/3603581.3603597},
        url = {https://doi.org/10.14778/3603581.3603597},
        year = {2023}
}

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