Reinforcement Learning-Guided Large Language Model Fine-Tuning for Privacy-Preserving Text Rewriting

Abstract

The advancement of machine learning systems depends on large-scale, high-quality datasets. However, corpora drawn from user-generated and proprietary domains are often laden with sensitive information, posing significant privacy, security, and compliance risks. Conventional anonymization methods, which focus on removing explicit identifiers, can degrade downstream performance and leave the more nuanced challenge of implicit privacy leakage unresolved. This form of leakage allows for sensitive attributes such as author identity, demographics, or personality to be inferred from writing style alone. To address this, we present a privacy-preserving text rewriting framework based on guided reinforcement learning. Our approach features a composite reward function that operates over disentangled semantic and stylistic representations to preserve utility while enforcing style convergence and author anonymity. Empirical validation demonstrates substantial improvements on implicit privacy metrics without sacrificing semantic fidelity, yielding a scalable, model-agnostic solution for privacy-preserving data generation in the age of Large Language Models.