A Demonstration of VisDPT: Visual Exploration of Differentially Private Trajectories
Summary: VisDPT interactively visualizes privacy risks and utility loss in differentially private trajectory releases. It helps curators interpret formal guarantees and compare sanitized mobility data against ground truth when deciding what to publish. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Xi He (Duke University)
- 2. Nisarg Raval (Duke University)
- 3. Ashwin Machanavajjhala (Duke University)
BibTeX Citation
@article{he_vldb16,
title = {{A Demonstration of VisDPT: Visual Exploration of Differentially Private Trajectories}},
author = {He, Xi and Raval, Nisarg and Machanavajjhala, Ashwin},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {13},
pages = {1489--1492},
doi = {10.14778/3007263.3007329},
url = {https://doi.org/10.14778/3007263.3007329},
year = {2016}
}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 221 | Robust and Fast Similarity Search for Moving Object Trajectories | 2005 | SIGMOD | 0.00024224879 |
| 3,871 | DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems | 2015 | VLDB | 7.0581665e-05 |
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