RASLIK uses randomized antipodal search on linearized influence kernels to achieve data Pareto improvement in LLM unlearning, outperforming baselines with sublinear complexity and double gains in quality and efficiency.
The secret sharer: Evaluating and testing unintended memorization in neural networks
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A new framework evaluates privacy metrics for synthetic tabular data by inserting controlled risks and testing detection under no-box threat models on public datasets.
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Randomized Antipodal Search Done Right for Data Pareto Improvement of LLM Unlearning
RASLIK uses randomized antipodal search on linearized influence kernels to achieve data Pareto improvement in LLM unlearning, outperforming baselines with sublinear complexity and double gains in quality and efficiency.
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Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework
A new framework evaluates privacy metrics for synthetic tabular data by inserting controlled risks and testing detection under no-box threat models on public datasets.