Real-World Trajectory Sharing with Local Differential Privacy
Summary: Presents a local-DP mechanism for trajectory sharing that perturbs hierarchical n-grams. Exploits a multi-dimensional POI-based hierarchy to boost realism and utility without privacy or efficiency costs; experiments on real data show superiority. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Teddy Cunningham (University of Warwick)
- 2. Graham Cormode (University of Warwick)
- 3. Hakan Ferhatosmanoglu (University of Warwick)
- 4. Divesh Srivastava (AT&T)
BibTeX Citation
@article{cunningham_vldb21,
title = {{Real-World Trajectory Sharing with Local Differential Privacy}},
author = {Cunningham, Teddy and Cormode, Graham and Ferhatosmanoglu, Hakan and Srivastava, Divesh},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2283--2295},
doi = {10.14778/3476249.3476280},
url = {https://doi.org/10.14778/3476249.3476280},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,885 | LDPTrace: Locally Differentially Private Trajectory Synthesis | 2023 | VLDB | 5.9559724e-05 |
| 7,577 | Benchmarking the Utility of w-event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors | 2023 | VLDB | 5.4914399e-05 |
| 10,742 | Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing | 2026 | VLDB | 4.9769913e-05 |
| 11,762 | Trajectory Data Collection with Local Differential Privacy | 2023 | VLDB | 4.9769913e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,119 | Differentially Private Event Sequences over Infinite Streams | 2014 | VLDB | 9.0081698e-05 |
| 2,586 | Privacy at Scale: Local Differential Privacy in Practice | 2018 | SIGMOD | 8.2533446e-05 |
| 3,939 | DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems | 2015 | VLDB | 6.9120876e-05 |
| 5,148 | A Rigorous and Customizable Framework for Privacy | 2012 | PODS | 6.2528693e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 3,149 | Private Queries in Location Based Services: Anonymizers are not Necessary | 2008 | SIGMOD |
| 2 | 12,156 | Top-k Queries over Digital Traces | 2019 | SIGMOD |
| 3 | 6,339 | A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy | 2023 | SIGMOD |
| 4 | 7,985 | Shortest Path Computation with No Information Leakage | 2012 | VLDB |
| 5 | 5,790 | A Neural Database for Differentially Private Spatial Range Queries | 2022 | VLDB |
| 6 | 12,365 | A Demonstration of VisDPT: Visual Exploration of Differentially Private Trajectories | 2016 | VLDB |
| 7 | 3,939 | DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems | 2015 | VLDB |
| 8 | 6,705 | A Deep Generative Model for Trajectory Modeling and Utilization | 2023 | VLDB |
| 9 | 11,762 | Trajectory Data Collection with Local Differential Privacy | 2023 | VLDB |
| 10 | 5,885 | LDPTrace: Locally Differentially Private Trajectory Synthesis | 2023 | VLDB |