Introduces natural identifiers (NIDs) from common training data to support post-hoc differential privacy auditing and dataset inference for LLMs without retraining or private held-out sets.
Membership inference attacks cannot prove that a model was trained on your data.arXiv preprint arXiv:2409.19798, 2024a
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Natural Identifiers for Privacy and Data Audits in Large Language Models
Introduces natural identifiers (NIDs) from common training data to support post-hoc differential privacy auditing and dataset inference for LLMs without retraining or private held-out sets.