Privacy-preserving Data Processing

Privacy-Preserving Data Processing is a computational paradigm within edge data processing that enables the analysis and utilization of sensitive data while ensuring confidentiality and individual privacy through techniques such as encryption, anonymization, or differential privacy.

470

Publications

24.5K

Citations

1.5K

Authors

580

Institutions

Publications per year

2017–2026

299

Authors

1.5K

  1. RL
    Rongxing Lu

    University of New Brunswick, Canada

    13 publications · 1K citations

    12

    h-index

  2. LT
    Laurence T. Yang

    St. Francis Xavier University, Canada

    10 publications · 408 citations

    10

    h-index

  3. XL
    Ximeng Liu

    Fuzhou University, China

    10 publications · 931 citations

    10

    h-index

  4. JC
    Jinjun Chen

    Swinburne University of Technology, Australia

    8 publications · 499 citations

    8

    h-index

  5. KR
    Kim‐Kwang Raymond Choo

    The University of Texas at San Antonio, United States

    7 publications · 450 citations

    7

    h-index

1–5 of 1.5K

Venues

  1. IEEE Internet of Things Journal

    19 publications · 964 citations

    16

    h-index

  2. IEEE Access

    16 publications · 1.5K citations

    14

    h-index

  3. Information Sciences

    9 publications · 477 citations

    9

    h-index

  4. IEEE Transactions on Industrial Informatics

    8 publications · 880 citations

    8

    h-index

  5. 8

    h-index

1–5