Data Anonymization
Data Anonymization is a data protection technique that belongs to the category of privacy-preserving methods, distinguished by its purpose of irreversibly removing or altering personally identifiable information to prevent the identification of individuals in health information systems.
5.1K
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352.5K
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2.7K
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2017–2026
2.3K
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Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song et al. · 2017 · 4.2K citations
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
Privacy-preserving data mining
Rakesh Agrawal, Ramakrishnan Srikant · ACM SIGMOD Record · 2000 · 3K citations
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Purdue University West Lafayette
West Lafayette, United States
55 h-index
217 publications
25.9K citations
Carnegie Mellon University
Pittsburgh, United States
40 h-index
130 publications
26.2K citations

Cornell University
Ithaca, United States
37 h-index
108 publications
57.5K citations

Xidian University
Xi'an, China
36 h-index
217 publications
12.3K citations

Pennsylvania State University
State College, United States
34 h-index
89 publications
10K citations
Nanyang Technological University
Singapore, Singapore
34 h-index
88 publications
8.1K citations

Universitat Rovira i Virgili
Tarragona, Spain
33 h-index
151 publications
7.5K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
32 h-index
75 publications
8.7K citations
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PVLDB · 67 publications · 7.7K citations
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TKDE · 85 publications · 10.4K citations
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Repository · 195 publications · 7.7K citations
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108 publications · 5K citations
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55 publications · 5.7K citations
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