Private Clustering
Private Clustering is a clustering methodology that belongs to the class of privacy-preserving data analysis techniques, distinguished by its incorporation of differential privacy guarantees to ensure that the output clusters do not reveal sensitive information about individual data points in the input dataset.
48
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1.7K
Citations
175
Authors
87
Institutions
Publications per year
2017–2026
32
Parents
48
Privacy-preserving distributed clustering using generative models
Srujana Merugu, Joydeep Ghosh · 2004 · 204 citations
Differentially Private K-Means Clustering
Dong Su, Jianneng Cao, Ninghui Li et al. · 2016 · 127 citations · Full text
Privacy of outsourced k-means clustering
Dongxi Liu, Elisa Bertino, Xun Yi · 2014 · 88 citations
Distributed K-Means clustering guaranteeing local differential privacy
Chang Xia, Jingyu Hua, Wei Tong et al. · Computers & Security · 2019 · 74 citations
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87

St. Francis Xavier University
Antigonish, Canada
3 h-index
4 publications
96 citations
Purdue University West Lafayette
West Lafayette, United States
3 h-index
7 publications
628 citations

Qatar University
Doha, Qatar
2 h-index
2 publications
74 citations
University of California, Berkeley
Berkeley, United States
2 h-index
2 publications
111 citations

North China Electric Power University
Beijing, China
2 h-index
7 publications
271 citations
Institute for Infocomm Research
Singapore, Singapore
2 h-index
2 publications
180 citations
Temple University
Philadelphia, United States
2 h-index
2 publications
74 citations

Technische Universität Darmstadt
Darmstadt, Germany
2 h-index
6 publications
174 citations
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3 publications · 88 citations
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2 publications · 123 citations
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Repository · 3 publications · 51 citations
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1 publications · 12 citations
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1 publications · 11 citations
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