Private Data Aggregation

Private Data Aggregation is a computational framework that enables the secure collection and summarization of sensitive data from distributed sources while preserving individual privacy through cryptographic or noise-based perturbation techniques.

94

Publications

6.2K

Citations

294

Authors

138

Institutions

Publications per year

2017–2026

48

Authors

294

  1. LF
    Liyue Fan

    University of Southern California, United States

    4 publications · 115 citations

    4

    h-index

  2. LX
    Li Xiong

    Emory University, United States

    3 publications · 82 citations

    3

    h-index

  3. YL
    Yining Liu

    Guilin University of Electronic Technology, China

    3 publications · 173 citations

    3

    h-index

  4. JC
    Jae‐Woo Chang

    Jeonbuk National University, South Korea

    3 publications · 48 citations

    3

    h-index

  5. RB
    Rabindra Bista

    Kathmandu University, Nepal

    3 publications · 48 citations

    3

    h-index

1–5 of 294

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 317 citations

    7

    h-index

  2. Information Fusion

    2 publications · 64 citations

    2

    h-index

  3. IEEE Internet of Things Journal

    2 publications · 77 citations

    2

    h-index

  4. IEEE Access

    2 publications · 37 citations

    2

    h-index

  5. IEEE Transactions on Dependable and Secure Computing

    TDSC · 2 publications · 168 citations

    2

    h-index

1–5