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A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing

Summary: Privacy framework for workers in spatial crowdsourcing using differential privacy and geocasting. Outperforms privacy methods; jointly optimizes completion rate, travel distance, and overhead, with real data showing privacy at low performance cost. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11158
Venue
VLDB
Year
2014
Pagerank
8.1294906e-05
Overall Rank
2,780 | 80.93%
DOI
10.14778/2732951.2732960

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{to_vldb14,
        title = {{A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing}},
        author = {To, Hien and Ghinita, Gabriel and Shahabi, Cyrus},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {10},
        pages = {919--930},
        doi = {10.14778/2732951.2732960},
        url = {https://doi.org/10.14778/2732951.2732960},
        year = {2014}
}

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