Anonymization For Graph Data
Anonymization for Graph Data is a data protection methodology that transforms graph-structured datasets by modifying their topological and attribute features to prevent the re-identification of individuals while preserving utility for analytical tasks.
123
Publications
8.1K
Citations
319
Authors
152
Institutions
Publications per year
2017–2026
42
Parents
123
Towards identity anonymization on graphs
Kun Liu, Evimaria Terzi · 2008 · 782 citations
James Cheng, Ada Wai-Chee Fu, Jia Liu · 2010 · 342 citations
Sharing graphs using differentially private graph models
Alessandra Sala, Xiaohan Zhao, Christo Wilson et al. · 2011 · 246 citations
1–5 of 123
319
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Indiana University – Purdue University Indianapolis, United States
5 publications · 112 citations
5
h-index
1–5 of 319
152
University of Illinois Chicago
Chicago, United States
8 h-index
11 publications
500 citations
Indiana University – Purdue University Indianapolis
Indianapolis, United States
7 h-index
13 publications
527 citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
16 publications
826 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
6 h-index
15 publications
798 citations

University of Victoria
Victoria, Canada
5 h-index
18 publications
796 citations
University of California, Santa Barbara
Santa Barbara, United States
5 h-index
21 publications
2.7K citations
Universitat Oberta de Catalunya
Barcelona, Spain
5 h-index
6 publications
187 citations
North Carolina State University
Raleigh, United States
4 h-index
7 publications
840 citations
1–8 of 152
TKDE · 5 publications · 142 citations
5
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5 publications · 367 citations
5
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Repository · 4 publications · 82 citations
4
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4 publications · 129 citations
4
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3 publications · 91 citations
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