Privacy-preserving Processing

Privacy-Preserving Processing is a computational methodology within edge-cloud integration that enables data analysis and service execution while safeguarding sensitive information through techniques such as encryption, differential privacy, or secure multi-party computation, thereby minimizing unauthorized access and ensuring compliance with privacy regulations.

474

Publications

26.3K

Citations

1.6K

Authors

560

Institutions

Publications per year

2017–2026

299

Authors

1.6K

  1. XL
    Ximeng Liu

    Fuzhou University, China

    12 publications · 1.1K citations

    12

    h-index

  2. KR
    Kim‐Kwang Raymond Choo

    The University of Texas at San Antonio, United States

    8 publications · 673 citations

    8

    h-index

  3. LT
    Laurence T. Yang

    St. Francis Xavier University, Canada

    8 publications · 505 citations

    8

    h-index

  4. RH
    Robert H. Deng

    Singapore Management University, Singapore

    7 publications · 719 citations

    7

    h-index

  5. XY
    Xun Yi

    RMIT University, Australia

    7 publications · 363 citations

    7

    h-index

1–5 of 1.6K

Venues

  1. arXiv (Cornell University)

    Repository · 20 publications · 1.3K citations

    16

    h-index

  2. IEEE Access

    17 publications · 899 citations

    16

    h-index

  3. IEEE Internet of Things Journal

    13 publications · 610 citations

    14

    h-index

  4. 11

    h-index

  5. IEEE Transactions on Dependable and Secure Computing

    TDSC · 7 publications · 739 citations

    7

    h-index

1–5