P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems

Abstract:Cloud-local LLM inference systems have the potential to use the reasoning capability of large cloud models while protecting sensitive user data on personal devices. Cloud-bound requests must exclude personally identifiable information (PII) to prevent external data leakage. Existing privacy-preserving methods rely on prompt perturbation, entity masking, or model fine-tuning, but these approaches 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 decomposition, PII-aware routing, paraphrasing, and reconstruction by following the skill prompts. Skills are iteratively refined from execution failures by a cloud LLM, enabling the local SLM to generalize beyond memorized PII patterns, and therefore P2Skill requires no privacy-specific fine-tuning or learned auxiliary detectors. Evaluation on a four-domain benchmark shows that P2Skill achieves $1.69\times$ and $3.66\times$ higher privacy-preserved inference quality than previous baselines.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2608.14094 [cs.CR]

(or arXiv:2608.14094v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2608.14094 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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