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)
Incoming Non-self Citations Over Time
Authors
- 1. Yuntao Du (Zhejiang University)
- 2. Yujia Hu (Zhejiang University)
- 3. Zhikun Zhang (Stanford University)
- 4. Ziquan Fang (Zhejiang University)
- 5. Lu Chen (Zhejiang University)
- 6. Baihua Zheng (Singapore Management University)
- 7. Yunjun Gao (Zhejiang University)
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}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,869 | Federated Heavy Hitter Analytics with Local Differential Privacy | 2025 | SIGMOD | 5.9620208e-05 |
| 9,731 | PrivRM: A Framework for Range Mean Estimation under Local Differential Privacy | 2025 | SIGMOD | 5.1325223e-05 |
| 10,645 | Efficient and Effective Biclique Counting with Local Differential Privacy | 2026 | SIGMOD | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 276 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.00022332903 |
| 1,453 | PrivBasis: Frequent Itemset Mining with Differential Privacy | 2012 | VLDB | 0.00010597751 |
| 2,533 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB | 8.3306153e-05 |
| 2,586 | Privacy at Scale: Local Differential Privacy in Practice | 2018 | SIGMOD | 8.2533446e-05 |
| 2,804 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD | 7.9817412e-05 |
| 3,205 | Frequency Estimation under Local Differential Privacy | 2021 | VLDB | 7.5370735e-05 |
| 3,292 | Kamino: Constraint-Aware Differentially Private Data Synthesis | 2021 | VLDB | 7.445638e-05 |
| 3,939 | DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems | 2015 | VLDB | 6.9120876e-05 |
| 5,462 | Real-World Trajectory Sharing with Local Differential Privacy | 2021 | VLDB | 6.1205281e-05 |
| 5,790 | A Neural Database for Differentially Private Spatial Range Queries | 2022 | VLDB | 5.9914362e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,671 | MTSP-LDP: A Framework for Multi-Task Streaming Data Publication under Local Differential Privacy | 2026 | SIGMOD |
| 2 | 3,237 | LDP-IDS: Local Differential Privacy for Infinite Data Streams | 2022 | SIGMOD |
| 3 | 13,961 | DesTeller: A System for Destination Prediction Based on Trajectories with Privacy Protection | 2013 | VLDB |
| 4 | 10,650 | Enhancing Local Differential Privacy Accuracy by Exploiting Inherent Uncertainty | 2026 | SIGMOD |
| 5 | 7,979 | TRACE: Real-time Compression of Streaming Trajectories in Road Networks | 2021 | VLDB |
| 6 | 6,705 | A Deep Generative Model for Trajectory Modeling and Utilization | 2023 | VLDB |
| 7 | 12,365 | A Demonstration of VisDPT: Visual Exploration of Differentially Private Trajectories | 2016 | VLDB |
| 8 | 3,939 | DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems | 2015 | VLDB |
| 9 | 11,762 | Trajectory Data Collection with Local Differential Privacy | 2023 | VLDB |
| 10 | 5,462 | Real-World Trajectory Sharing with Local Differential Privacy | 2021 | VLDB |