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LDPTrace: Locally Differentially Private Trajectory Synthesis

Summary: LDPTrace: an LDP trajectory synthesis framework that infers three local mobility patterns to generate realistic paths with low computation and no external priors. Introduces privacy-preserving grid-granularity selection and demonstrates superior utility and attack robustness on real/synthetic data. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13233
Venue
VLDB
Year
2023
Pagerank
6.0955612e-05
Overall Rank
5,763 | 60.47%
DOI
10.14778/3594512.3594520

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{du_vldb23,
        title = {{LDPTrace: Locally Differentially Private Trajectory Synthesis}},
        author = {Du, Yuntao and Hu, Yujia and Zhang, Zhikun and Fang, Ziquan and Chen, Lu and Zheng, Baihua and Gao, Yunjun},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {8},
        pages = {1897--1909},
        doi = {10.14778/3594512.3594520},
        url = {https://doi.org/10.14778/3594512.3594520},
        year = {2023}
}

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