PriSTE: Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services
Summary: PriSTE addresses privacy leakage from continuous location releases, protecting sensitive spatiotemporal events that conventional LPPMs miss. Its interactive demo exposes inference risks and supports automatic/manual LPPM conversion with privacy–utility visualization. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yang Cao (Kyoto University)
- 2. Yonghui Xiao (Google)
- 3. Li Xiong (Emory University)
- 4. Liquan Bai (Emory University)
- 5. Masatoshi Yoshikawa (Kyoto University)
BibTeX Citation
@article{cao_vldb19,
title = {{PriSTE: Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services}},
author = {Cao, Yang and Xiao, Yonghui and Xiong, Li and Bai, Liquan and Yoshikawa, Masatoshi},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {1866--1869},
doi = {10.14778/3352063.3352086},
url = {https://doi.org/10.14778/3352063.3352086},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,120 | PANDA: Policy-aware Location Privacy for Epidemic Surveillance | 2020 | VLDB | 4.9793485e-05 |
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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 |
|---|---|---|---|---|
| 12,307 | LocLok: Location Cloaking with Differential Privacy via Hidden Markov Model | 2017 | VLDB | 4.9793485e-05 |
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