Where every claim in SecProve
comes from.

Introduced DP-SGD for training neural networks with formal differential privacy guarantees. Foundation for private ML.

First practical membership inference attack against ML models. Showed that ML APIs leak information about their training data.

Voluntary framework for improving privacy through enterprise risk management. Complements the Cybersecurity Framework.

Demonstrated that LLMs memorize and can be prompted to regurgitate training data verbatim, including PII. Foundational work on LLM privacy risks.

Introduced SISA training for efficient machine unlearning — enabling models to "forget" specific training data without full retraining.

Open-source DP libraries and practical guides. Bridges theory to implementation. Good for questions on real-world DP deployment challenges and privacy budget management.

Extracted training data from ChatGPT (production model) using a divergence attack. Showed alignment doesn't prevent memorization. Questions on the gap between safety fine-tuning and data protection.

The theoretical foundation for differential privacy. Essential for questions on privacy-preserving ML training (DP-SGD) and the epsilon-delta framework.

Extended training data extraction to image models. Showed Stable Diffusion memorizes and regurgitates training images. Important for multimodal AI data security questions.