K-anonymity
K-Anonymity is a privacy-preserving data anonymization model that ensures each individual in a dataset cannot be distinguished from at least k−1 other individuals with respect to their quasi-identifying attributes.
586
Publications
60.2K
Citations
1.4K
Authors
556
Institutions
Publications per year
2017–2026
177
586
Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke et al. · ACM Transactions on Knowledge Discovery from Data · 2007 · 3.6K citations
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian · 2007 · 3.4K citations
L-diversity: privacy beyond k-anonymity
Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer et al. · 2006 · 2.4K citations
1–5 of 586
1.4K
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1–5 of 1.4K
556

Universitat Rovira i Virgili
Tarragona, Spain
21 h-index
58 publications
3.6K citations
Purdue University West Lafayette
West Lafayette, United States
20 h-index
49 publications
11.3K citations

University of Southern Queensland
Toowoomba, Australia
12 h-index
29 publications
1.7K citations
University of Illinois Chicago
Chicago, United States
11 h-index
17 publications
667 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
11 h-index
17 publications
2.1K citations
National University of Singapore
Singapore, Singapore
10 h-index
18 publications
2.8K citations

University of South Australia
Adelaide, Australia
10 h-index
10 publications
326 citations
Carnegie Mellon University
Pittsburgh, United States
9 h-index
13 publications
11.8K citations
1–8 of 556
TKDE · 24 publications · 4.5K citations
21
h-index
19 publications · 631 citations
14
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Repository · 6 publications · 165 citations
6
h-index
Repository · 5 publications · 123 citations
5
h-index
5 publications · 87 citations
5
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1–5