ConfusionPrompt: Practical Private Inference for Online Large Language Models
As the deployment of state-of-the-art large language models (LLMs) as online services becomes more prevalent, the privacy of user data has emerged as a significant concern. Users are often required to transmit detailed prompts to cloud servers, leaving their sensitive information exposed. To address these privacy issues, researchers have introduced a cutting-edge framework known as ConfusionPrompt.
Understanding ConfusionPrompt
ConfusionPrompt is designed to enhance privacy during the inference process of LLMs. The framework operates on two main principles:
- Decomposing the original prompt into smaller, more manageable sub-prompts.
- Generating pseudo-prompts alongside the genuine sub-prompts sent to the LLM.
Once the server processes these prompts, the user can recombine the responses to generate the final output. This unique approach sets ConfusionPrompt apart from traditional methods of protecting user privacy, offering several key advantages.
Key Advantages of ConfusionPrompt
ConfusionPrompt presents a variety of benefits compared to previous privacy protection methods for LLMs:
- Seamless Integration: The framework integrates effortlessly with existing black-box LLMs, ensuring that users can adopt it without needing to overhaul their current systems.
- Improved Privacy-Utility Trade-off: ConfusionPrompt significantly enhances the balance between privacy and utility, outperforming existing text perturbation methods.
Privacy Model and Complexity Analysis
To establish a robust foundation for ConfusionPrompt, the researchers developed a privacy model referred to as the $(\lambda, \mu, \rho)$-privacy model. This model delineates the requirements necessary for a privacy-preserving group of prompts, ensuring that users’ data remains secure throughout the inference process.
Additionally, a complexity analysis was conducted to validate the effectiveness of prompt decomposition. This analysis illustrates how breaking down prompts can lead to more efficient and secure processing without compromising the quality of the output.
Empirical Evaluation
The efficacy of ConfusionPrompt has been substantiated through empirical evaluation. Results indicate that it achieves a significantly higher utility than local inference methods utilizing open-source models and perturbation-based techniques. Furthermore, the framework reduces memory consumption compared to typical open-source LLMs, making it a more efficient solution for users concerned about both privacy and performance.
Conclusion
In conclusion, ConfusionPrompt represents a significant advancement in privacy-preserving inference for online large language models. By effectively decomposing prompts and generating pseudo-prompts, it not only enhances user privacy but also improves the overall utility of LLMs in practical applications. As the need for secure data handling continues to grow, frameworks like ConfusionPrompt will play a crucial role in shaping the future of AI-driven technologies.