PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
<|reference_start|>PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining: 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.<|reference_end|>
@article{kazmi2024panoramia:,
    title={PANORAMIA: Privacy Auditing of Machine Learning Models without
  Retraining},
    author={Mishaal Kazmi, Hadrien Lautraite, Alireza Akbari, Qiaoyue Tang,
  Mauricio Soroco, Tao Wang, S'ebastien Gambs, Mathias L'ecuyer},
    journal={arXiv preprint arXiv:2402.09477},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09477},
    primaryClass={cs.CR cs.LG}
}