Privacy-preserving Data Analytics
Privacy-Preserving Data Analytics is a computational framework that enables the extraction of meaningful patterns and insights from mobile health data while ensuring individual privacy through techniques such as data anonymization, encryption, or differential privacy to prevent unauthorized re-identification and data leakage.
236
Publications
17.2K
Citations
946
Authors
380
Institutions
Publications per year
2017–2026
133
Parents
236
Frank McSherry · 2009 · 1.1K citations
Tools for privacy preserving distributed data mining
Chris Clifton, Murat Kantarcıoğlu, Jaideep Vaidya et al. · ACM SIGKDD Explorations Newsletter · 2002 · 890 citations
State-of-the-art in privacy preserving data mining
Vassilios S. Verykios, Elisa Bertino, Igor Nai Fovino et al. · ACM SIGMOD Record · 2004 · 813 citations
1–5 of 236
946
5
h-index
4
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4
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École Polytechnique Fédérale de Lausanne, Switzerland
3 publications · 296 citations
3
h-index
3
h-index
1–5 of 946
380
University of California San Diego
San Diego, United States
8 h-index
35 publications
3K citations

Vanderbilt University
Nashville, United States
7 h-index
14 publications
519 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
5 h-index
17 publications
906 citations
Harvard University
Cambridge, United States
5 h-index
5 publications
329 citations
Microsoft (United States)
Redmond, United States
5 h-index
5 publications
1.7K citations
RMIT University
Melbourne, Australia
5 h-index
14 publications
959 citations

University of Manitoba
Winnipeg, Canada
5 h-index
9 publications
186 citations

Huazhong University of Science and Technology
Wuhan, China
5 h-index
8 publications
694 citations
1–8 of 380
Repository · 7 publications · 200 citations
7
h-index
5 publications · 412 citations
5
h-index
5 publications · 421 citations
4
h-index
JAMIA · 4 publications · 294 citations
4
h-index
PVLDB · 4 publications · 530 citations
4
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1–5