User Preference Protection

User Preference Protection is a privacy-preserving mechanism in recommendation systems that specifically safeguards the confidentiality and integrity of individual users' behavioral and declarative preference data against unauthorized inference or reconstruction during model training and inference.

100

Publications

4.4K

Citations

311

Authors

155

Institutions

Publications per year

2017–2026

45

Authors

311

  1. HP
    Hüseyin Polat

    Anadolu University, Türkiye

    8 publications · 472 citations

    8

    h-index

  2. AB
    Alper Bilge

    Anadolu University, Türkiye

    4 publications · 100 citations

    4

    h-index

  3. AM
    Ahmed M. Elmisery

    Waterford Institute of Technology, Ireland

    3 publications · 40 citations

    3

    h-index

  4. XX
    Xiaolong Xu

    Nanjing University of Information Science and Technology, China

    3 publications · 132 citations

    3

    h-index

  5. ES
    Erez Shmueli

    Tel Aviv University, Israel

    3 publications · 93 citations

    3

    h-index

1–5 of 311

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 114 citations

    3

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 367 citations

    3

    h-index

  3. Information Sciences

    3 publications · 68 citations

    3

    h-index

  4. Expert Systems with Applications

    3 publications · 140 citations

    3

    h-index

  5. Future Generation Computer Systems

    FGCS · 3 publications · 142 citations

    3

    h-index

1–5