A General Framework for Data-Use Auditing of ML Models
<|reference_start|>A General Framework for Data-Use Auditing of ML Models: Auditing the use of data in training machine-learning (ML) models is an increasingly pressing challenge, as myriad ML practitioners routinely leverage the effort of content creators to train models without their permission. In this paper, we propose a general method to audit an ML model for the use of a data-owner's data in training, without prior knowledge of the ML task for which the data might be used. Our method leverages any existing black-box membership inference method, together with a sequential hypothesis test of our own design, to detect data use with a quantifiable, tunable false-detection rate. We show the effectiveness of our proposed framework by applying it to audit data use in two types of ML models, namely image classifiers and foundation models.<|reference_end|>
@article{huang2024a,
    title={A General Framework for Data-Use Auditing of ML Models},
    author={Zonghao Huang, Neil Zhenqiang Gong, Michael K. Reiter},
    journal={arXiv preprint arXiv:2407.15100},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15100},
    primaryClass={cs.CR cs.LG}
}