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

Authors

100

  1. KC
    Kamalika Chaudhuri

    University of California San Diego, United States

    4 publications · 1.6K citations

    4

    h-index

  2. JF
    Jeffrey F. Naughton

    University of Wisconsin–Madison, United States

    3 publications · 207 citations

    3

    h-index

  3. SJ
    Somesh Jha

    University of Wisconsin–Madison, United States

    3 publications · 207 citations

    3

    h-index

  4. XZ
    Xiaojin Zhu

    University of Wisconsin–Madison, United States

    2 publications · 133 citations

    2

    h-index

  5. DS
    Dong Su

    Purdue University West Lafayette, United States

    2 publications · 107 citations

    2

    h-index

1–5 of 100

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 691 citations

    9

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 65 citations

    2

    h-index

  3. Journal of Privacy and Confidentiality

    JPC · 2 publications · 43 citations

    2

    h-index

  4. IEEE Transactions on Big Data

    2 publications · 123 citations

    2

    h-index

  5. Transactions on data privacy

    1 publications · 26 citations

    1

    h-index

1–5