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PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
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We present PANORAMIA, a privacy leakage measurement framework for machine learning models that relies on membership inference attacks using generated data as non-members. By relying on generated non-member data, PANORAMIA eliminates the common dependency of privacy measurement tools on in-distribution non-member data. As a result, PANORAMIA does not modify the model, training data, or training process, and only requires access to a subset of the training data. We evaluate PANORAMIA on ML models for image and tabular data classification, as well as on large-scale language models.
Forward citations
Cited by 3 Pith papers
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In-Context Probing for Membership Inference in Fine-Tuned Language Models
ICP-MIA infers membership in fine-tuned LLMs by measuring confidence improvement under in-context probes, beating prior black-box attacks at low false-positive rates.
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Ensembling Membership Inference Attacks Against Tabular Generative Models
No single membership inference attack dominates across tabular generative models, and unsupervised ensembles of attacks achieve better average rankings.
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Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble
MIAs expose different members depending on attack method and random seed; the paper quantifies this with coverage/stability and shows ensembling attacks yields stronger, more reliable privacy checks.
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