T-closeness
T-Closeness is a privacy-preserving data anonymization model that extends l-diversity by requiring that the distribution of sensitive attribute values within any equivalence class is statistically close to the distribution in the overall dataset, differing by no more than a threshold t, thereby mitigating re-identification and attribute disclosure risks.
35
Publications
4.9K
Citations
91
Authors
45
Institutions
Publications per year
2017–2026
11
35
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian · 2007 · 3.4K citations
A Critique of k-Anonymity and Some of Its Enhancements
Vicenç Torra · 2008 · 153 citations
1–5 of 35
91
4
h-index
3
h-index
Sardar Vallabhbhai National Institute of Technology Surat, India
1 publications · 13 citations
2
h-index
2
h-index
2
h-index
1–5 of 91
45

Universitat Rovira i Virgili
Tarragona, Spain
6 h-index
14 publications
1K citations
Purdue University West Lafayette
West Lafayette, United States
2 h-index
4 publications
7.1K citations
Universitat Politècnica de Catalunya
Barcelona, Spain
2 h-index
5 publications
426 citations

Sardar Vallabhbhai National Institute of Technology Surat
Surat, India
2 h-index
1 publications
13 citations

University of Electro-Communications
Tokyo, Japan
2 h-index
6 publications
274 citations
Chandigarh University
Mohali, India
2 h-index
1 publications
13 citations
AT&T (United States)
Bedminster, United States
2 h-index
2 publications
3.4K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
2 publications
143 citations
1–8 of 45
TKDE · 3 publications · 486 citations
3
h-index
TKDD · 1 publications · 84 citations
1
h-index
1 publications · 17 citations
1
h-index
1 publications · 47 citations
1
h-index
TDSC · 1 publications · 91 citations
1
h-index
1–5