Privacy-preserving Computing

Privacy-Preserving Computing is a computational paradigm that enables data processing and analysis while safeguarding sensitive information by ensuring that raw data remains confidential and inaccessible during computation, thereby balancing utility and privacy in distributed edge-cloud environments.

1.3K

Publications

62.6K

Citations

3.5K

Authors

1K

Institutions

Publications per year

2017–2026

798

Authors

3.5K

  1. XL
    Ximeng Liu

    Fuzhou University, China

    24 publications · 2.1K citations

    21

    h-index

  2. RL
    Rongxing Lu

    University of New Brunswick, Canada

    23 publications · 1.9K citations

    20

    h-index

  3. RH
    Robert H. Deng

    Singapore Management University, Singapore

    18 publications · 1.1K citations

    18

    h-index

  4. KR
    Kim‐Kwang Raymond Choo

    The University of Texas at San Antonio, United States

    15 publications · 1.1K citations

    16

    h-index

  5. CW
    Cong Wang

    City University of Hong Kong, Hong Kong SAR China

    15 publications · 2.4K citations

    15

    h-index

1–5 of 3.5K

Venues

  1. IEEE Internet of Things Journal

    55 publications · 3.2K citations

    35

    h-index

  2. IEEE Access

    40 publications · 2.8K citations

    25

    h-index

  3. 24

    h-index

  4. IEEE Transactions on Dependable and Secure Computing

    TDSC · 30 publications · 1.8K citations

    22

    h-index

  5. arXiv (Cornell University)

    Repository · 28 publications · 1.4K citations

    19

    h-index

1–5