P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems
Cloud-local LLM inference can use the reasoning capability of large cloud models, but cloud-bound requests must exclude personally identifiable information (PII). Existing privacy-preserving methods modify the prompt or the model, which may distort contextual semantics or require additional training. This paper proposes P2Skill, a prompt-based skill distillation method in which a local small language model (SLM) autonomously performs four skills. A cloud LLM iteratively refines the skills from execution failures to let the local SLM generalize beyond memorized PII patterns, and the final skills are selected at the lowest validation leakage within set bounds. P2Skill requires no privacy-sp...
| Authors | Myunghoon Ryu, Geunpyo Park, Sungjoon Lee et al. |
|---|---|
| Primary category | cs.CR |
| Published | 14 August 2026 |
| Last updated at source | 13 October 2026 |
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