  Cloud-based large language models (LLMs) such as ChatGPT have increasingly
become integral to daily operations, serving as vital tools across various
applications. While these models offer substantial benefits in terms of
accessibility and functionality, they also introduce significant privacy
concerns: the transmission and storage of user data in cloud infrastructures
pose substantial risks of data breaches and unauthorized access to sensitive
information; even if the transmission and storage of data is encrypted, the LLM
service provider itself still knows the real contents of the data, preventing
individuals or entities from confidently using such LLM services. To address
these concerns, this paper proposes a simple yet effective mechanism EmojiCrypt
to protect user privacy. It uses Emoji to encrypt the user inputs before
sending them to LLM, effectively rendering them indecipherable to human or
LLM's examination while retaining the original intent of the prompt, thus
ensuring the model's performance remains unaffected. We conduct experiments on
three tasks, personalized recommendation, sentiment analysis, and tabular data
analysis. Experiment results reveal that EmojiCrypt can encrypt personal
information within prompts in such a manner that not only prevents the
discernment of sensitive data by humans or LLM itself, but also maintains or
even improves the precision without further tuning, achieving comparable or
even better task accuracy than directly prompting the LLM without prompt
encryption. These results highlight the practicality of adopting encryption
measures that safeguard user privacy without compromising the functional
integrity and performance of LLMs. Code and dataset are available at
https://github.com/agiresearch/EmojiCrypt.
