Privacy-preserving Data Aggregation

Privacy-Preserving Data Aggregation is a data aggregation method that enables the collection and analysis of aggregate-level information from distributed IoT devices while ensuring individual data contributors' privacy through cryptographic, perturbation-based, or anonymization techniques.

496

Publications

29.1K

Citations

1.3K

Authors

515

Institutions

Publications per year

2017–2026

299

Authors

1.3K

  1. RL
    Rongxing Lu

    University of New Brunswick, Canada

    28 publications · 2.4K citations

    23

    h-index

  2. XS
    Xuemin Shen

    University of Waterloo, Canada

    13 publications · 1.7K citations

    13

    h-index

  3. YL
    Yining Liu

    Guilin University of Electronic Technology, China

    15 publications · 1K citations

    13

    h-index

  4. XL
    Xiaodong Lin

    University of Waterloo, Canada

    13 publications · 939 citations

    13

    h-index

  5. ZC
    Zhenfu Cao

    Shanghai Jiao Tong University, China

    11 publications · 1.3K citations

    11

    h-index

1–5 of 1.3K

Venues

  1. IEEE Internet of Things Journal

    37 publications · 2.2K citations

    29

    h-index

  2. IEEE Access

    18 publications · 1.2K citations

    16

    h-index

  3. IEEE Transactions on Industrial Informatics

    15 publications · 1.7K citations

    14

    h-index

  4. 13

    h-index

  5. IEEE Systems Journal

    ISJ · 13 publications · 581 citations

    12

    h-index

1–5