Differentially Private Statistics
Differentially Private Statistics is a statistical methodology that belongs to the class of privacy-preserving analytical techniques, distinguished by its formal guarantee that the inclusion or exclusion of a single individual’s data in a dataset does not significantly affect the probability distribution of the statistical output, thereby providing rigorous, mathematically provable privacy protections.
140
Publications
9.1K
Citations
254
Authors
119
Institutions
Publications per year
2017–2026
75
Parents
140
Differential privacy and robust statistics
Cynthia Dwork, Jing Lei · 2009 · 634 citations
The reusable holdout: Preserving validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt et al. · Science · 2015 · 319 citations
Computing, Data Science, Data Mining +14
Trading Private Range Counting over Big IoT Data
Zhipeng Cai, Zaobo He · 2019 · 314 citations
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254
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119

Pennsylvania State University
State College, United States
12 h-index
21 publications
1.5K citations

Harvard University Press
Cambridge, United States
11 h-index
13 publications
305 citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
11 publications
531 citations
Google (United States)
Mountain View, United States
8 h-index
10 publications
3.6K citations
Microsoft (United States)
Redmond, United States
7 h-index
9 publications
1.6K citations
University of Pennsylvania
Philadelphia, United States
7 h-index
10 publications
532 citations

Cornell University
Ithaca, United States
6 h-index
12 publications
473 citations
United States Census Bureau
Suitland, United States
6 h-index
8 publications
574 citations
1–8 of 119
Repository · 32 publications · 697 citations
18
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JPC · 15 publications · 700 citations
12
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PVLDB · 4 publications · 422 citations
4
h-index
3 publications · 65 citations
3
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3 publications · 193 citations
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