PrivacyPAD: A Reinforcement Learning Framework for Dynamic Privacy-Aware Delegation

• URL: http://arxiv.org/abs/2510.16054v1
• Date: Thu, 16 Oct 2025 19:38:36 GMT
• Title: PrivacyPAD: A Reinforcement Learning Framework for Dynamic Privacy-Aware Delegation
• Authors: Zheng Hui, Yijiang River Dong, Sanhanat Sivapiromrat, Ehsan Shareghi, Nigel Collier,
• Abstract summary: We introduce a novel reinforcement learning framework called PrivacyPAD to solve this problem.<n>Our framework trains an agent to dynamically route text chunks, learning a policy that optimally balances the trade-off between privacy leakage and task performance.<n>Our framework achieves a new state-of-the-art on the privacy-utility frontier.
• Score: 33.37227619820212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send the query to a powerful proprietary LLM providers to achieving state-of-the-art performance and risk data exposure, or relying on smaller, local models guarantees data privacy but often results in a degradation of task performance. Prior approaches have relied on static pipelines that use LLM rewriting, which shatters linguistic coherence and indiscriminately removes privacy-sensitive information, including task-critical content. We reformulate this challenge (Privacy-Conscious Delegation) as a sequential decision-making problem and introduce a novel reinforcement learning (RL) framework called PrivacyPAD to solve it. Our framework trains an agent to dynamically route text chunks, learning a policy that optimally balances the trade-off between privacy leakage and task performance. It implicitly distinguishes between replaceable Personally Identifiable Information (PII) (which it shields locally) and task-critical PII (which it strategically sends to the remote model for maximal utility). To validate our approach in complex scenarios, we also introduce a new medical dataset with high PII density. Our framework achieves a new state-of-the-art on the privacy-utility frontier, demonstrating the necessity of learned, adaptive policies for deploying LLMs in sensitive environments.

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