Tabular Data Protection
Tabular Data Protection is a methodological framework within disclosure protection that specifically safeguards confidential information in tabular data by applying statistical and computational techniques to prevent the unauthorized inference of sensitive attributes while preserving data utility for legitimate analysis.
51
Publications
2.7K
Citations
88
Authors
61
Institutions
Publications per year
2017–2026
8
Parents
51
Anatomy: simple and effective privacy preservation
Xiaokui Xiao, Yufei Tao · Very Large Data Bases · 2006 · 646 citations
Computing, Anatomy, Computer Science +16
Privacy, accuracy, and consistency too
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork et al. · 2007 · 487 citations
Health Professions, Information Systems, Data Management +10
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61
Duke University
Durham, United States
5 h-index
8 publications
237 citations

Universidad de La Laguna
San Cristóbal de La Laguna, Spain
5 h-index
5 publications
166 citations
Universitat Politècnica de Catalunya
Barcelona, Spain
4 h-index
4 publications
127 citations

Universitat Rovira i Virgili
Tarragona, Spain
4 h-index
7 publications
197 citations

Research Triangle Park Foundation
Durham, United States
3 h-index
5 publications
181 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
6 publications
228 citations

University of Padua
Padua, Italy
3 h-index
3 publications
116 citations
National Institute of Statistical Sciences
Washington D.C., United States
3 h-index
6 publications
168 citations
1–8 of 61
8 publications · 452 citations
8
h-index
IJUFKS · 3 publications · 66 citations
3
h-index
2 publications · 89 citations
2
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Repository · 1 publications · 12 citations
1
h-index
1 publications · 12 citations
1
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