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)

Paper ID: 12994
Venue: VLDB
Year: 2023
Pagerank: 4.9563668e-05
Overall Rank: 6,700 | 53.40%
DOI: 10.14778/3583140.3583146

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,932	P-Shapley: Shapley Values on Probabilistic Classifiers	2024	VLDB	4.613363e-05
10,686	PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning	2025	VLDB	4.1945683e-05
10,716	Federated and Balanced Clustering for High-dimensional Data	2025	VLDB	4.1945683e-05
11,219	F3 KM: Federated, Fair, and Fast k-means	2023	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
568	Practical Privacy: The SuLQ Framework	2005	PODS	0.00019949368
1,143	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00013710269
2,555	Answering Multi-Dimensional Analytical Queries under Local Differential Privacy	2019	SIGMOD	8.5477878e-05
4,679	Locating a Small Cluster Privately	2016	PODS	6.0044653e-05
5,775	Federated Matrix Factorization with Privacy Guarantee	2022	VLDB	5.3310992e-05

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1,143	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00013710269
4,805	Projected Federated Averaging with Heterogeneous Differential Privacy	2022	VLDB	5.9102798e-05
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