SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models
With the widespread deployment of public large language models (LLMs) such as ChatGPT, protecting user prompt privacy has become an increasingly critical issue. Existing privacy-preserving inference methods sacrifice either utility or efficiency, and often require model-specific modifications that limit their compatibility. In this paper, we propose SharedRequest, a model-agnostic framework for privacy-preserving LLM inference that reformulates privacy protection at the batch level rather than the individual-prompt level. The key idea is to obscure sensitive information by mixing original prompts with noisy variants, while grouping semantically equivalent instructions to amortize the infe...
| Authors | Peihua Mai, Xuanrong Gao, Youlong Ding et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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