PrivSTD: Differentially Private Spatio-temporal Trajectory Density Data Publication
Summary: PrivSTD enables differentially private release of high-resolution spatio-temporal densities in the frequency domain, preserving correlated low-frequency structure. FDR-based truncation and control-variate R2R denoising suppress noise and substantially improve reconstruction utility. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Shuzhan Ye (Zhejiang University)
- 2. Yujia Hu (Zhejiang University)
- 3. Lu Chen (Zhejiang University)
- 4. Yangyang Wu (Zhejiang University)
- 5. Zhikun Zhang (Zhejiang University)
- 6. Tianyi Li (Aalborg University)
- 7. Christian S. Jensen (Aalborg University)
BibTeX Citation
@article{ye_vldb26,
title = {{PrivSTD: Differentially Private Spatio-temporal Trajectory Density Data Publication}},
author = {Ye, Shuzhan and Hu, Yujia and Chen, Lu and Wu, Yangyang and Zhang, Zhikun and Li, Tianyi and Jensen, Christian S.},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {10},
pages = {2713--2726},
doi = {10.14778/3828612.3828626},
url = {https://doi.org/10.14778/3828612.3828626},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 558 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.00016470707 |
| 732 | Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption | 2010 | SIGMOD | 0.00014383875 |
| 3,711 | Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics | 2017 | SIGMOD | 7.0783969e-05 |
| 5,789 | A Neural Database for Differentially Private Spatial Range Queries | 2022 | VLDB | 5.9942738e-05 |
| 6,335 | A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy | 2023 | SIGMOD | 5.8100198e-05 |
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