Privacy-preserving Data Mining

Privacy-Preserving Data Mining is a computational methodology within data mining that integrates techniques to protect individual data privacy while enabling the extraction of meaningful patterns and knowledge from datasets.

951

Publications

65.7K

Citations

2K

Authors

767

Institutions

Publications per year

2017–2026

268

Authors

2K

  1. CC
    Chris Clifton

    Purdue University West Lafayette, United States

    28 publications · 5.5K citations

    25

    h-index

  2. JV
    Jaideep Vaidya

    Rutgers, The State University of New Jersey, United States

    17 publications · 4.2K citations

    17

    h-index

  3. JC
    Jerry Chun‐Wei Lin

    Western Norway University of Applied Sciences, Norway

    19 publications · 730 citations

    16

    h-index

  4. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    16 publications · 1.5K citations

    15

    h-index

  5. VS
    Vassilios S. Verykios

    Hellenic Open University, Greece

    15 publications · 2.7K citations

    13

    h-index

1–5 of 2K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 18 publications · 5.2K citations

    17

    h-index

  2. IEEE Access

    19 publications · 1.5K citations

    15

    h-index

  3. Information Sciences

    14 publications · 705 citations

    13

    h-index

  4. Data & Knowledge Engineering

    DKE · 12 publications · 539 citations

    11

    h-index

  5. Expert Systems with Applications

    11 publications · 424 citations

    11

    h-index

1–5