Brain Surgery: Ensuring GDPR Compliance in Large Language Models via Concept Erasure

TL;DR

This paper addresses the challenge of making large language models GDPR-compliant by enabling targeted unlearning and real-time privacy management at the local model level. It introduces Brain Surgery, a modular framework that combines Embedding-Corrupted Prompts (ECO Prompts), privacy-aware continual learning, and blockchain-based privacy management to erase sensitive data with minimal impact on model performance. Key contributions include a formal ECO Prompts formulation, a conflict-score mechanism to preserve related knowledge, and a real-time privacy monitoring loop with user-defined privacy preferences. The approach promises scalable, verifiable privacy compliance for diverse AI deployments, empowering users and organizations with transparent governance and ethical data handling in AI systems.

Abstract

As large-scale AI systems proliferate, ensuring compliance with data privacy laws such as the General Data Protection Regulation (GDPR) has become critical. This paper introduces Brain Surgery, a transformative methodology for making every local AI model GDPR-ready by enabling real-time privacy management and targeted unlearning. Building on advanced techniques such as Embedding-Corrupted Prompts (ECO Prompts), blockchain-based privacy management, and privacy-aware continual learning, Brain Surgery provides a modular solution that can be deployed across various AI architectures. This tool not only ensures compliance with privacy regulations but also empowers users to define their own privacy limits, creating a new paradigm in AI ethics and governance.