PrivacyScalpel is a novel framework that precisely removes Personally Identifiable Information from Large Language Models while preserving their overall utility.

• Uses sparse autoencoders to identify and isolate features representing private information
• Applies targeted feature intervention rather than broad neuron-level approaches
• Achieves superior privacy protection compared to existing methods
• Maintains model performance on standard language tasks

This research matters for security professionals by offering a practical solution to one of the most challenging privacy trade-offs in AI deployment: protecting sensitive data without degrading model capabilities.

PrivacyScalpel: Enhancing LLM Privacy via Interpretable Feature Intervention with Sparse Autoencoders