Privacy-aware Task Scheduling

Privacy-Aware Task Scheduling is a computational scheduling framework that, as a member of the task scheduling paradigms in edge computing, specifically incorporates privacy preservation as a core optimization criterion by dynamically allocating tasks to edge nodes while minimizing privacy exposure through data localization, access control, and anonymization mechanisms.

37

Publications

1.4K

Citations

145

Authors

82

Institutions

Publications per year

2017–2026

28

Authors

145

  1. XX
    Xiaolong Xu

    Nanjing University of Information Science and Technology, China

    2 publications · 134 citations

    2

    h-index

  2. IA
    Imad Aad

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 56 citations

    2

    h-index

  3. XH
    Xiaofan He

    North Carolina State University, United States

    2 publications · 181 citations

    2

    h-index

  4. HD
    Huaiyu Dai

    North Carolina State University, United States

    2 publications · 181 citations

    2

    h-index

  5. RJ
    Richeng Jin

    North Carolina State University, United States

    2 publications · 181 citations

    2

    h-index

1–5 of 145

Venues

  1. Future Generation Computer Systems

    FGCS · 5 publications · 250 citations

    5

    h-index

  2. Information Sciences

    3 publications · 77 citations

    3

    h-index

  3. 2

    h-index

  4. IEEE Transactions on Vehicular Technology

    1 publications · 13 citations

    1

    h-index

  5. Peer-to-Peer Networking and Applications

    1 publications · 22 citations

    1

    h-index

1–5