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