Local Sensitivity

Local Sensitivity is a differential privacy metric that quantifies the maximum change in the output of a query function over neighboring datasets within a localized subset of the data domain, distinguishing it from global sensitivity by depending on the specific input rather than the entire dataset space.

192

Publications

8.1K

Citations

572

Authors

299

Institutions

Publications per year

2017–2026

66

Authors

572

  1. HX
    Hui Xie

    University of Illinois Chicago, United States

    6 publications · 120 citations

    6

    h-index

  2. DF
    Daniel F. Heitjan

    University of Pennsylvania, United States

    5 publications · 178 citations

    5

    h-index

  3. TT
    Tamás Turányi

    Eötvös Loránd University, Hungary

    4 publications · 80 citations

    4

    h-index

  4. PG
    Paul Gustafson

    University of British Columbia, Canada

    4 publications · 146 citations

    4

    h-index

  5. NM
    Noah M. Johnson

    University of California, Berkeley, United States

    3 publications · 125 citations

    3

    h-index

1–5 of 572

Venues

  1. Statistics in Medicine

    5 publications · 140 citations

    5

    h-index

  2. 4

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 108 citations

    4

    h-index

  4. 3

    h-index

  5. Reliability Engineering & System Safety

    3 publications · 81 citations

    3

    h-index

1–5