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Set-valued Data Publication with Local Privacy: Tight Error Bounds and Efficient Mechanisms

Summary: Wheel mechanism enables utility-optimal LDP publication of high-dimensional set-valued data, with each user sending one numerical value. It achieves optimal Θ(md/(nε²)) error, O(min{m log m, me^ε}) computation, and O(log(me^ε)) communication. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12227
Venue
VLDB
Year
2020
Pagerank
5.3804383e-05
Overall Rank
8,707 | 40.27%
DOI
10.14778/3389133.3389140

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb20,
        title = {{Set-valued Data Publication with Local Privacy: Tight Error Bounds and Efficient Mechanisms}},
        author = {Wang, Shaowei and Qian, Yuqiu and Du, Jiachun and Yang, Wei and Huang, Liusheng and Xu, Hongli},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {8},
        pages = {1234--1247},
        doi = {10.14778/3389133.3389140},
        url = {https://doi.org/10.14778/3389133.3389140},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
4,132 Answering Multi-Dimensional Range Queries under Local Differential Privacy 2021 VLDB 6.8832571e-05
5,595 Privacy Amplification via Shuffling: Unified, Simplified, and Tightened 2024 VLDB 6.1548101e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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