# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 5 Conclusions  
In this study, in response to the challenges of protecting personal privacy in dialogue-based large models in the information era, an innovative framework for privacy-preserving dialogue-based large models is presented, effectively integrating FHE and mechanisms. The primary task of the proposed framework is to enhance the responsiveness and accuracy of dialogue systems without compromising user privacy. The novelties of this  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Security** & **Precision** & **Recall** & **Accuracy** & **Latency (ms)** \\ \hline Multi-head Attention [4] & \(\bigstar\) & 0.89 & 0.87 & 0.89 & 0.13 \\ Dynamic Sparse Attention & \(\checkmark\) & 0.92 & 0.91 & 0.92 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison on different attention mechanisms.  
paper are manifested in the combination of FHE technology with dialogue-based large models, enabling the processing of user data without decryption. Thus, even if data leakage occurs during processing and transmission, sensitive user information is not exposed. The introduced module optimizes processing efficiency and computational resources compared to traditional multi-head attention mechanisms by only calculating the attention scores between key elements that significantly affect the output, thereby reducing unnecessary computations.  
In the experimental section, a comparative analysis of different models on the performance impact of natural language processing tasks while protecting privacy yielded the following results:  
1. While maintaining the performance of transformers, the proposed method achieved security comparable to FHE with minimal latency of 0.15 ms, demonstrating efficient encryption technology.
2. In Section 4.2, the analysis of latency overhead, the proposed method effectively controlled and optimized the latency in computation, communication, and encryption/decryption, with results of 0.05 ms, 0 ms, and 0.08 ms, respectively.
3. The ablation study, in Section 4.3, on the DSA module showed that it outperforms the multi-head attention mechanism in terms of precision, recall, and accuracy which are 0.92, 0.91, and 0.92, respectively, with a negligible increase in latency of 0.15 ms.  
However, there are still shortcomings in this research. For instance, although the mechanism reduces computational load, it may miss important information when selecting key elements. Future research plans will unfold in several directions: 1. Optimizing encryption algorithms to further reduce latency and enhance performance. 2. Improving the mechanism to increase accuracy in selecting key elements, ensuring no crucial information is missed. 3. Cross-domain applications to demonstrate the utility and scalability of the framework in various fields, such as online education and legal consultation.  
In conclusion, this paper provides a new direction for research on privacy-preserving dialogue-based large models and has empirically demonstrated the effectiveness of the proposed methods. Future studies will continue to delve deeper, refining the model to provide stronger security for user privacy and to promote the healthy development of the artificial intelligence field.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.