Privacy-preserving Data Analytics

Privacy-Preserving Data Analytics is a computational framework that enables the extraction of meaningful patterns and insights from mobile health data while ensuring individual privacy through techniques such as data anonymization, encryption, or differential privacy to prevent unauthorized re-identification and data leakage.

236

Publications

17.2K

Citations

946

Authors

380

Institutions

Publications per year

2017–2026

133

Authors

946

  1. BM
    Bradley Malin

    Vanderbilt University, United States

    5 publications · 146 citations

    5

    h-index

  2. XJ
    Xiaoqian Jiang

    University of California San Diego, United States

    4 publications · 220 citations

    4

    h-index

  3. AA
    Adeel Anjum

    COMSATS University Islamabad, Pakistan

    4 publications · 140 citations

    4

    h-index

  4. JL
    Jean Louis Raisaro

    École Polytechnique Fédérale de Lausanne, Switzerland

    3 publications · 296 citations

    3

    h-index

  5. AC
    Alfredo Cuzzocrea

    University of Calabria, Italy

    3 publications · 85 citations

    3

    h-index

1–5 of 946

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 200 citations

    7

    h-index

  2. Information Sciences

    5 publications · 412 citations

    5

    h-index

  3. IEEE Access

    5 publications · 421 citations

    4

    h-index

  4. Journal of the American Medical Informatics Association

    JAMIA · 4 publications · 294 citations

    4

    h-index

  5. Proceedings of the VLDB Endowment

    PVLDB · 4 publications · 530 citations

    4

    h-index

1–5