Data Minimization
Data Minimization is a foundational privacy principle that requires the collection, processing, and retention of personal data to be strictly limited to the minimum extent necessary for the specified legitimate purpose.
344
Publications
25.3K
Citations
843
Authors
380
Institutions
Publications per year
2017–2026
154
344
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent et al. · Foundations and Trends® in Machine Learning · 2020 · 4.7K citations · Full text
Local Privacy and Statistical Minimax Rates
John C. Duchi, Michael I. Jordan, Martin J. Wainwright · 2013 · 990 citations
On the hardness of approximating minimization problems
Carsten Lund, Mihalis Yannakakis · Journal of the ACM · 1994 · 890 citations · Full text
1–5 of 344
843
9
h-index
University of California, Berkeley, United States
8 publications · 1.6K citations
8
h-index
7
h-index
5
h-index
5
h-index
1–5 of 843
380
University of California, Berkeley
Berkeley, United States
10 h-index
22 publications
4.7K citations
Arizona State University
Tempe, United States
10 h-index
20 publications
435 citations
Stanford University
Stanford, United States
8 h-index
15 publications
7.1K citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
17 publications
723 citations
Google (United States)
Mountain View, United States
7 h-index
16 publications
721 citations

Centre National de la Recherche Scientifique
Paris, France
7 h-index
11 publications
1.4K citations
Nanyang Technological University
Singapore, Singapore
6 h-index
13 publications
1.1K citations
University of Illinois Urbana-Champaign
Urbana, United States
6 h-index
6 publications
589 citations
1–8 of 380
Repository · 30 publications · 1.4K citations
18
h-index
9 publications · 603 citations
8
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Conference · 7 publications · 166 citations
7
h-index
7 publications · 509 citations
7
h-index
6 publications · 571 citations
6
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1–5