Adversarial Privacy Attacks

Adversarial Privacy Attacks is a category of security threats in federated learning wherein malicious entities exploit model updates or system vulnerabilities to infer sensitive private information about individual participants, distinguished from other privacy risks by their deliberate, targeted nature and reliance on adversarial strategies to compromise data confidentiality.

102

Publications

8.1K

Citations

372

Authors

160

Institutions

Publications per year

2017–2026

94

Authors

372

  1. FT
    Florian Tramèr

    Stanford University, United States

    5 publications · 353 citations

    5

    h-index

  2. SJ
    Somesh Jha

    University of Wisconsin–Madison, United States

    4 publications · 2.9K citations

    4

    h-index

  3. NC
    Nicholas Carlini

    Google (United States), United States

    4 publications · 338 citations

    4

    h-index

  4. MM
    Mohammad Mahmoody

    University of Virginia, United States

    3 publications · 63 citations

    3

    h-index

  5. RA
    Raluca Ada Popa

    University of California, Berkeley, United States

    3 publications · 166 citations

    3

    h-index

1–5 of 372

Venues

  1. arXiv (Cornell University)

    Repository · 18 publications · 899 citations

    13

    h-index

  2. Computer Standards & Interfaces

    3 publications · 134 citations

    3

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 171 citations

    3

    h-index

  4. IEEE Access

    2 publications · 318 citations

    2

    h-index

  5. 2

    h-index

1–5