Summary of Emojicrypt: Prompt Encryption For Secure Communication with Large Language Models, by Guo Lin et al.
EmojiCrypt: Prompt Encryption for Secure Communication with Large Language Models
by Guo Lin, Wenyue Hua, Yongfeng Zhang
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a mechanism called EmojiCrypt to protect user privacy in cloud-based large language models (LLMs) such as ChatGPT. As LLMs become integral to daily operations, concerns about data breaches and unauthorized access to sensitive information are growing. EmojiCrypt uses emojis to encrypt user inputs before sending them to the LLM, rendering them indecipherable to humans or the model itself. The authors conduct experiments on three tasks: personalized recommendation, sentiment analysis, and tabular data analysis. Results show that EmojiCrypt maintains or improves task accuracy while preventing sensitive data discernment, achieving comparable or better performance than direct prompting without encryption. This highlights the practicality of adopting encryption measures for user privacy protection in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using a powerful language model like ChatGPT to get helpful answers, but worrying about your personal information being shared or accessed without permission. This paper develops a new way to keep your data private called EmojiCrypt. It uses emojis to scramble the information before sending it to the model, making it unreadable for anyone else. The authors tested this method on three different tasks and found that it works just as well as using the model normally, but with the added protection of keeping your data safe. |
Keywords
* Artificial intelligence * Language model * Prompting
