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Game-theoretic framework enhances LLM knowledge acquisition while preserving privacy

Researchers have developed a new framework called Game-theoretic Trustworthy Knowledge Acquisition (GTKA) to address the privacy concerns associated with using cloud-hosted Large Language Models (LLMs). GTKA formulates the trade-off between knowledge utility and privacy as a strategic game, decomposing sensitive user intents into generalized fragments to minimize leakage. The framework includes a privacy-aware generator, an adversarial attacker to provide leakage signals, and a local integrator for synthesizing responses. Experiments on biomedical and legal datasets show GTKA effectively reduces intent leakage while maintaining answer quality. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Offers a novel approach to balance LLM utility with user privacy, potentially enabling more secure use of external models for sensitive data.

RANK_REASON Academic paper introducing a new framework for LLM knowledge acquisition.

Read on arXiv cs.CL →

COVERAGE [1]

  1. arXiv cs.CL TIER_1 · Rujing Yao, Yufei Shi, Yang Wu, Ang Li, Zhuoren Jiang, XiaoFeng Wang, Haixu Tang, Xiaozhong Liu ·

    Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition

    arXiv:2604.23413v1 Announce Type: new Abstract: Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these external services risks exposing sensitive user intent. Conversely, relying exclusively on t…