Private Statistical Estimation

Private Statistical Estimation is a statistical inference framework that belongs to the class of differentially private data analysis methods, distinguished by its specific design to estimate population parameters from sensitive data while providing rigorous mathematical guarantees of privacy preservation through the controlled addition of noise or other privacy-preserving perturbations.

58

Publications

2.9K

Citations

146

Authors

89

Institutions

Publications per year

2017–2026

35

Authors

146

  1. GK
    Gautam Kamath

    Massachusetts Institute of Technology, United States

    6 publications · 110 citations

    6

    h-index

  2. JU
    Jonathan Ullman

    Harvard University, United States

    5 publications · 94 citations

    5

    h-index

  3. AS
    Adam Smith

    Pennsylvania State University, United States

    4 publications · 633 citations

    4

    h-index

  4. OS
    Or Sheffet

    Carnegie Mellon University, United States

    3 publications · 48 citations

    3

    h-index

  5. XT
    Xiaoying Tian

    Ocean University of China, China

    2 publications · 142 citations

    2

    h-index

1–5 of 146

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 271 citations

    10

    h-index

  2. Journal of Privacy and Confidentiality

    JPC · 4 publications · 325 citations

    4

    h-index

  3. The Annals of Statistics

    4 publications · 220 citations

    4

    h-index

  4. Neural Information Processing Systems

    Conference · 3 publications · 126 citations

    3

    h-index

  5. Conference on Learning Theory

    Conference · 2 publications · 67 citations

    2

    h-index

1–5