Privacy-preserving Computation

Privacy-Preserving Computation is a computational paradigm that enables data processing and analysis while safeguarding individual privacy by ensuring that sensitive information remains confidential and inaccessible to unauthorized entities throughout the computation process.

1.7K

Publications

106.1K

Citations

4.1K

Authors

1.1K

Institutions

Publications per year

2017–2026

881

Authors

4.1K

  1. XL
    Ximeng Liu

    Fuzhou University, China

    35 publications · 2.5K citations

    28

    h-index

  2. RH
    Robert H. Deng

    Singapore Management University, Singapore

    28 publications · 1.6K citations

    22

    h-index

  3. RL
    Rongxing Lu

    University of New Brunswick, Canada

    28 publications · 1.9K citations

    21

    h-index

  4. KR
    Kim‐Kwang Raymond Choo

    The University of Texas at San Antonio, United States

    24 publications · 1.5K citations

    21

    h-index

  5. KR
    Kui Ren

    University at Buffalo, State University of New York, United States

    20 publications · 1.6K citations

    18

    h-index

1–5 of 4.1K

Venues

  1. 33

    h-index

  2. IEEE Internet of Things Journal

    34 publications · 1.6K citations

    25

    h-index

  3. IEEE Transactions on Dependable and Secure Computing

    TDSC · 40 publications · 2.1K citations

    24

    h-index

  4. IEEE Access

    39 publications · 1.5K citations

    23

    h-index

  5. arXiv (Cornell University)

    Repository · 60 publications · 2.3K citations

    23

    h-index

1–5