Data Minimization

Data Minimization is a foundational privacy principle that requires the collection, processing, and retention of personal data to be strictly limited to the minimum extent necessary for the specified legitimate purpose.

344

Publications

25.3K

Citations

843

Authors

380

Institutions

Publications per year

2017–2026

154

Authors

843

  1. JC
    John C. Duchi

    Stanford University, United States

    9 publications · 1.6K citations

    9

    h-index

  2. MJ
    Martin J. Wainwright

    University of California, Berkeley, United States

    8 publications · 1.6K citations

    8

    h-index

  3. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    7 publications · 1.6K citations

    7

    h-index

  4. LS
    Lalitha Sankar

    Arizona State University, United States

    5 publications · 121 citations

    5

    h-index

  5. KT
    Kunal Talwar

    Microsoft (United States), United States

    5 publications · 281 citations

    5

    h-index

1–5 of 843

Venues

  1. arXiv (Cornell University)

    Repository · 30 publications · 1.4K citations

    18

    h-index

  2. 8

    h-index

  3. International Conference on Machine Learning

    Conference · 7 publications · 166 citations

    7

    h-index

  4. IEEE Transactions on Signal Processing

    7 publications · 509 citations

    7

    h-index

  5. 6

    h-index

1–5