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Numerous companies have started offering services based on large languagemodels (LLM), such as ChatGPT, which inevitably raises privacy concerns asusers' prompts are exposed to the model provider. Previous research on securereasoning using multi-party computation (MPC) has proven to be impractical forLLM applications due to its time-consuming and communication-intensive nature.While lightweight anonymization techniques can protect private information inprompts through substitution or masking, they fail to recover sensitive datareplaced in the LLM-generated results. In this paper, we expand the applicationscenarios of anonymization techniques by training a small local model tode-anonymize the LLM's returned results with minimal computational overhead. Weintroduce the HaS framework, where "H(ide)" and "S(eek)" represent its two coreprocesses: hiding private entities for anonymization and seeking privateentities for de-anonymization, respectively. To quantitatively assess HaS'sprivacy protection performance, we propose both black-box and white-boxadversarial models. Furthermore, we conduct experiments to evaluate HaS'susability in translation and classification tasks. The experimental findingsdemonstrate that the HaS framework achieves an optimal balance between privacyprotection and utility.

Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection