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Privacy exposure displacement, the mismatch between a local evaluation proxy and target-grounded session exposure, and ASLEval, an authorization-aware framework that pre-registers a hidden target set, measures all declared visible exits, and reserves internal traces for diagnosis are introduced.
ProSPy achieves over 60% execution accuracy in Text-to-SQL tasks, effectively bridging the gap between SQL efficiency and Python flexibility for complex enterprise databases.
By bounding conditioning representation magnitude, DP-aware AdaLN-Zero tames heavy-tailed gradients in differentially private diffusion models, leading to improved performance under strict privacy budgets.
By strategically allocating differential privacy based on layer-specific vulnerability, LM-DP-SGD significantly improves the privacy-utility trade-off against membership inference attacks on intermediate representations.
