Federated Analytics Privacy

Federated Analytics Privacy is a privacy-preserving framework within federated learning that enables the secure and decentralized computation of aggregate statistics across distributed client devices while ensuring individual data contributions remain confidential and inaccessible to external parties.

6

Publications

1.6K

Citations

23

Authors

20

Institutions

Publications per year

2017–2026

6

Authors

23

  1. BS
    Benjamin S. Glicksberg

    Icahn School of Medicine at Mount Sinai, United States

    2 publications · 1.5K citations

    2

    h-index

  2. PW
    Peter Walker

    University of Bath, United Kingdom

    2 publications · 1.5K citations

    2

    h-index

  3. JB
    Jiang Bian

    University of Florida, United States

    2 publications · 1.5K citations

    2

    h-index

  4. JX
    Jie Xu

    Cornell University, United States

    2 publications · 1.5K citations

    2

    h-index

  5. CS
    Chang Su

    Cornell University, United States

    2 publications · 1.5K citations

    2

    h-index

1–5 of 23

Venues

  1. arXiv (Cornell University)

    Repository · 1 publications · 67 citations

    1

    h-index

  2. Journal of Healthcare Informatics Research

    1 publications · 1.4K citations

    1

    h-index

  3. IEEE Network

    1 publications · 46 citations

    1

    h-index

  4. 2022 IEEE Symposium on Security and Privacy (SP)

    Conference · 1 publications · 25 citations

    1

    h-index

  5. Big Data and Cognitive Computing

    1 publications · 30 citations

    1

    h-index

1–5