Output Perturbation
Output Perturbation is a disclosure protection technique that involves modifying the results of queries or analyses to prevent the identification of sensitive information while preserving the overall utility of the data.
41
Publications
4K
Citations
100
Authors
53
Institutions
Publications per year
2017–2026
23
Variants
Output Perturbation Method
Parents
41
Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, Adam Smith · 2007 · 1K citations
Differentially Private Empirical Risk Minimization.
Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate · PubMed · 2011 · 865 citations · Full text
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Xi Wu, Fengan Li, Arun Kumar et al. · 2017 · 177 citations
Vibhor Rastogi, Michael Hay, Gerome Miklau et al. · 2009 · 138 citations
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53
University of Wisconsin–Madison
Madison, United States
4 h-index
14 publications
1.2K citations
University of California San Diego
San Diego, United States
4 h-index
6 publications
1.8K citations

Pennsylvania State University
State College, United States
3 h-index
7 publications
2.1K citations

Imperial College London
London, United Kingdom
2 h-index
3 publications
83 citations
Google (United States)
Mountain View, United States
2 h-index
6 publications
567 citations
Nanjing University of Posts and Telecommunications
Nanjing, China
2 h-index
4 publications
143 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
6 publications
456 citations
University of Washington
Seattle, United States
2 h-index
5 publications
324 citations
1–8 of 53
Repository · 9 publications · 691 citations
9
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PVLDB · 2 publications · 65 citations
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JPC · 2 publications · 43 citations
2
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2 publications · 123 citations
2
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1 publications · 26 citations
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