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

Authors

254

  1. CD
    Cynthia Dwork

    IBM Research - Almaden, United States

    11 publications · 1.6K citations

    11

    h-index

  2. JU
    Jonathan Ullman

    Harvard University, United States

    7 publications · 390 citations

    7

    h-index

  3. LX
    Li Xiong

    Emory University, United States

    6 publications · 445 citations

    6

    h-index

  4. AR
    Aaron Roth

    University of Pennsylvania, United States

    5 publications · 409 citations

    5

    h-index

  5. AS
    Adam Smith

    Pennsylvania State University, United States

    5 publications · 904 citations

    5

    h-index

1–5 of 254

Venues

  1. arXiv (Cornell University)

    Repository · 32 publications · 697 citations

    18

    h-index

  2. Journal of Privacy and Confidentiality

    JPC · 15 publications · 700 citations

    12

    h-index

  3. Proceedings of the VLDB Endowment

    PVLDB · 4 publications · 422 citations

    4

    h-index

  4. Harvard Data Science Review

    3 publications · 65 citations

    3

    h-index

  5. The Annals of Statistics

    3 publications · 193 citations

    3

    h-index

1–5