PANORAMIA: Privacy Auditing of Machine Learning Models without
  Retraining
Mishaal Kazmi Hadrien Lautraite Alireza Akbari Mauricio Soroco Qiaoyue Tang Tao Wang Sébastien Gambs Mathias Lécuyer
We introduce a privacy auditing scheme for ML models that relies on
membership inference attacks using generated data as "non-members". This
scheme, which we call PANORAMIA, quantifies the privacy leakage for large-scale
ML models without control of the training process or model re-training and only
requires access to a subset of the training data. To demonstrate its
applicability, we evaluate our auditing scheme across multiple ML domains,
ranging from image and tabular data classification to large-scale language
models.