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Locating a Small Cluster Privately

Summary: New differentially private algorithm to locate small point clusters, enabling private identification of dense subsets and outlier removal. Relaxes sample-and-aggregate requirements to enable broader conversion of off-the-shelf analyses into differentially private versions. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1691
Venue
PODS
Year
2016
Pagerank
6.0044653e-05
Overall Rank
4,679 | 67.46%
DOI
10.1145/2902251.2902296

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,700 Differentially Private Vertical Federated Clustering 2023 VLDB 4.9563668e-05
8,522 Differentially Private Hierarchical Heavy Hitters 2024 PODS 4.4937074e-05
9,697 PCOR: Private Contextual Outlier Release via Differentially Private Search 2021 SIGMOD 4.3022295e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,681 GUPT: Privacy Preserving Data Analysis Made Easy 2012 SIGMOD 0.00010929746
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10,470 Approximate DBSCAN under Differential Privacy 2025 SIGMOD 4.1945683e-05
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