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This paper analyzes the privacy-utility trade-off in LLM interactions, uncovering underlying mechanisms and introducing an intent-driven local protection framework with a lightweight model to enhance privacy while maintaining response utility.
Proposes Complementary Self-Distillation (SelfCI) to improve contextual integrity in LLMs by balancing utility and privacy. Evaluated on CI-RL and PrivacyLens benchmarks across multiple models.
This paper introduces Attention-Shifting (AS), a novel framework for selective machine unlearning in LLMs that balances effective removal of sensitive information while preventing hallucinations and preserving model utility. The method uses importance-aware attention suppression and retention enhancement to achieve up to 15% higher accuracy preservation compared to existing unlearning approaches on standard benchmarks.