Tabular Data Protection

Tabular Data Protection is a methodological framework within disclosure protection that specifically safeguards confidential information in tabular data by applying statistical and computational techniques to prevent the unauthorized inference of sensitive attributes while preserving data utility for legitimate analysis.

51

Publications

2.7K

Citations

88

Authors

61

Institutions

Publications per year

2017–2026

8

Authors

88

  1. JC
    Jordi Castro

    Universitat Politècnica de Catalunya, Spain

    4 publications · 127 citations

    4

    h-index

  2. JS
    Juan‐José Salazar‐González

    Universidad de La Laguna, Spain

    4 publications · 108 citations

    4

    h-index

  3. AD
    Adrian Dobra

    University of Washington, United States

    3 publications · 91 citations

    4

    h-index

  4. VT
    Vicenç Torra

    Artificial Intelligence Research Institute, Spain

    3 publications · 56 citations

    3

    h-index

  5. AF
    Alan F. Karr

    National Institute of Statistical Sciences, United States

    2 publications · 56 citations

    3

    h-index

1–5 of 88

Venues

  1. 8

    h-index

  2. 3

    h-index

  3. European Journal of Operational Research

    2 publications · 89 citations

    2

    h-index

  4. 1

    h-index

  5. Statistical Journal of the IAOS

    1 publications · 12 citations

    1

    h-index

1–5