Private Clustering

Private Clustering is a clustering methodology that belongs to the class of privacy-preserving data analysis techniques, distinguished by its incorporation of differential privacy guarantees to ensure that the output clusters do not reveal sensitive information about individual data points in the input dataset.

48

Publications

1.7K

Citations

175

Authors

87

Institutions

Publications per year

2017–2026

32

Authors

175

  1. EB
    Elisa Bertino

    Purdue University West Lafayette, United States

    3 publications · 268 citations

    3

    h-index

  2. US
    Uri Stemmer

    Ben-Gurion University of the Negev, Israel

    3 publications · 83 citations

    3

    h-index

  3. KN
    Kobbi Nissim

    Ben-Gurion University of the Negev, Israel

    3 publications · 83 citations

    3

    h-index

  4. HM
    Helen Möllering

    Technische Universität Darmstadt, Germany

    2 publications · 60 citations

    2

    h-index

  5. QZ
    Qingchen Zhang

    St. Francis Xavier University, Canada

    2 publications · 41 citations

    2

    h-index

1–5 of 175

Venues

  1. IEEE Access

    3 publications · 88 citations

    3

    h-index

  2. Computers & Security

    2 publications · 123 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 51 citations

    2

    h-index

  4. 1

    h-index

  5. ETRI Journal

    1 publications · 11 citations

    1

    h-index

1–5