Privacy-preserving Distributed Analytics

Privacy-Preserving Distributed Analytics is a distributed data analysis paradigm that enables multiple parties to collaboratively derive insights from decentralized data sources while enforcing privacy-preserving mechanisms to prevent the exposure of sensitive information during computation.

40

Publications

4K

Citations

154

Authors

85

Institutions

Publications per year

2017–2026

23

Authors

154

  1. LX
    Li Xiong

    Emory University, United States

    2 publications · 43 citations

    2

    h-index

  2. JR
    Juan Ramón Troncoso-Pastoriza

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 217 citations

    2

    h-index

  3. JL
    Jean Louis Raisaro

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 217 citations

    2

    h-index

  4. LO
    Lucila Ohno‐Machado

    University of California San Diego, United States

    2 publications · 63 citations

    2

    h-index

  5. JZ
    Joshua Zhexue Huang

    Shenzhen University, China

    2 publications · 43 citations

    2

    h-index

1–5 of 154

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 174 citations

    2

    h-index

  2. Computer

    2 publications · 64 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 81 citations

    2

    h-index

  4. IEEE Transactions on Dependable and Secure Computing

    TDSC · 2 publications · 50 citations

    2

    h-index

  5. Management Science

    1 publications · 18 citations

    1

    h-index

1–5