Differentially Private Vertical Federated Clustering
Summary: Practical DP vertical‑federated k‑means with an untrusted server: parties send DP local centers and DP membership encodings; server builds a weighted grid synopsis and runs k‑means. Novel DP set‑intersection via Flajolet‑Martin and refined weight estimation yield provable utility and lower loss than baselines. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zitao Li (Purdue University)
- 2. Tianhao Wang (University of Virginia)
- 3. Ninghui Li (Purdue University)
BibTeX Citation
@article{li_vldb23,
title = {{Differentially Private Vertical Federated Clustering}},
author = {Li, Zitao and Wang, Tianhao and Li, Ninghui},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {6},
pages = {1277--1290},
doi = {10.14778/3583140.3583146},
url = {https://doi.org/10.14778/3583140.3583146},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,250 | P-Shapley: Shapley Values on Probabilistic Classifiers | 2024 | VLDB | 5.4574671e-05 |
| 10,934 | PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning | 2025 | VLDB | 5.093636e-05 |
| 10,959 | Federated and Balanced Clustering for High-dimensional Data | 2025 | VLDB | 5.093636e-05 |
| 11,420 | F3 KM: Federated, Fair, and Fast k-means | 2023 | SIGMOD | 5.093636e-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.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 510 | Practical Privacy: The SuLQ Framework | 2005 | PODS | 0.00017220509 |
| 1,249 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB | 0.00011495357 |
| 2,279 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.8155688e-05 |
| 3,693 | Locating a Small Cluster Privately | 2016 | PODS | 7.1990589e-05 |
| 5,314 | Optimal Differentially Private Algorithms for k-Means Clustering | 2018 | PODS | 6.2688519e-05 |
| 5,588 | Federated Matrix Factorization with Privacy Guarantee | 2022 | VLDB | 6.1564686e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,045 | Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation | 2024 | VLDB |
| 2 | 10,959 | Federated and Balanced Clustering for High-dimensional Data | 2025 | VLDB |
| 3 | 11,252 | Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy | 2024 | VLDB |
| 4 | 5,746 | Federated Heavy Hitter Analytics with Local Differential Privacy | 2025 | SIGMOD |
| 5 | 3,237 | BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | 2022 | SIGMOD |
| 6 | 10,934 | PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning | 2025 | VLDB |
| 7 | 1,249 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB |
| 8 | 5,314 | Optimal Differentially Private Algorithms for k-Means Clustering | 2018 | PODS |
| 9 | 4,446 | Projected Federated Averaging with Heterogeneous Differential Privacy | 2022 | VLDB |
| 10 | 5,588 | Federated Matrix Factorization with Privacy Guarantee | 2022 | VLDB |