Auditing Information Disclosure During Large-Scale Gradient-Based Training via Gradient Uniqueness

arXiv:2510.10902 · cs.LG, stat.ML · Submitted 2025-10-13 · Read on arXiv

cs.LG, stat.ML

Submitted: 2025-10-13

Updated: 2026-09-27

Code: https://github.com/SleemJunior/gradient_uniqueness

Terminology

• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Differential Privacy — 2× in this paper · explained in 4 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• AI Alignment — 1× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• IEEE Symposium on Security and Privacy — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Membership Inference Attacks (MIAs) — 1× in this paper · explained in 2 episodes
• Non-Gaussianity — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• On the Opportunities and Risks of Foundation Models
• Quantifying Memorization Across Neural Language Models
• Membership Inference Attacks From First Principles
• The Files are in the Computer: On Copyright, Memorization, and Generative AI
• What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
• Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
• Pointer Sentinel Mixture Models
• Scalable Extraction of Training Data from (Production) Language Models
• Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning
• Non-Gaussianity of Stochastic Gradient Noise
• Canary Extraction in Natural Language Understanding Models
• Privacy Auditing with One (1) Training Run
• Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models
• Pandora's White-Box: Precise Training Data Detection and Extraction in Large Language Models
• Data Shapley in One Training Run
• Bag of Tricks for Training Data Extraction from Language Models
• Counterfactual Memorization in Neural Language Models
• Quantifying and Analyzing Entity-level Memorization in Large Language Models

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