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Auditing Information Disclosure During Large-Scale Gradient-Based Training via Gradient Uniqueness
Auditing information disclosure across every datapoint during the training of LLMs is challenging. We propose a principled, attack-agnostic approach that uses mutual information to measure what the final model reveals about a datapoint's training membership. We show that this final-model disclosure
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