#prompt-privacy News & Analysis

2 articles tagged with #prompt-privacy. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · Jun 47/10

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SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models

SharedRequest introduces a privacy-preserving inference framework for large language models that protects user prompt privacy by mixing prompts with noisy variants at the batch level, rather than individual-prompt level. The model-agnostic approach achieves 20% higher utility than differential privacy baselines while reducing query costs by up to 5x, requiring no modifications to LLM architecture.

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AINeutralarXiv – CS AI · Apr 106/10

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Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation

Researchers introduce Privacy-Preserving Fine-Tuning (PPFT), a novel training approach that enables LLM services to process user queries without receiving raw text, addressing privacy vulnerabilities in current deployments. The method uses client-side encoders and noise-injected embeddings to maintain competitive model performance while eliminating exposure of sensitive personal, medical, or legal information.