## 4 Results and Discussion
### Computational Efficiency Analysis  
The purpose of this experiment was to analyze the efficiency of different privacy-preserving methods in practical computational environments, particularly in devices with limited computational power. By comparing the FPS performance of MPC, HE, DP, FL, and the proposed method across different hardware platforms, such as Raspberry Pi, Jetson, and 3080 GPU, the experiment aims to reveal the computational efficiency differences \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **Accuracy** & **mAP@50** & **mAP@75** & **Computation Complexity** \\ \hline Multi-party Secure Computation & 0.84 & 0.80 & 0.82 & 0.82 & 0.81 & 0.80 & 27/30 \\ Homomorphic Encryption & 0.86 & 0.82 & 0.84 & 0.84 & 0.84 & 0.83 & 215.81 \\ Differential Privacy & 0.89 & 0.86 & 0.87 & 0.87 & 0.88 & 0.87 & 11.54 \\ Federated Learning & 0.91 & 0.88 & 0.89 & 0.89 & 0.90 & 0.89 & 63.96 \\ Proposed Method & 0.93 & 0.90 & 0.91 & 0.91 & 0.91 & 0.90 & 7.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Image data experiment results. between these models. The goal is to select the most suitable privacy-preserving method for real-world applications. Computational efficiency directly impacts the feasibility and performance of privacy-preserving technologies in large-scale applications, especially in scenarios where real-time data streams need to be processed efficiently. The experimental results provide a comprehensive evaluation of how each method performs under different hardware configurations and offer theoretical insights into how privacy protection can be balanced with computational efficiency. As observed from the experimental results presented in Table 5, the FPS of all methods significantly improved as the computational platform's performance increased, with the highest performance achieved when using high-performance GPUs (3080 GPU). On the Raspberry Pi platform, due to its limited computational power, FPS values were generally lower, with the proposed method showing an FPS of 29.93, slightly ahead of the other methods, but still noticeably lower than the results from high-performance platforms. The performance on the Jetson platform, which lies between the Raspberry Pi and 3080 GPU, showed better efficiency, with the proposed method achieving an FPS of 46.64, surpassing most traditional privacy-preserving methods. Particularly on the 3080 GPU, the proposed method achieved an FPS of 57.89, significantly outpacing the other models, especially when compared to FL (48.39 FPS) and DP (51.48 FPS), showing a much higher computational efficiency. This demonstrates that the proposed method optimizes the balance between privacy protection and computational efficiency, especially when leveraging high-performance computing platforms. Theoretically, the advantage of the proposed method can be attributed to its design, which combines symmetric projection and adversarial training to effectively reduce computational overhead while maintaining strong privacy protection. This optimization is particularly evident on high-performance hardware, where the ability to handle the increased computational load allows the method to demonstrate superior efficiency. From a mathematical perspective, the reduction in computational complexity is achieved by minimizing the amount of redundant information that needs to be processed during the privacy-preserving transformation. Traditional privacy-preserving methods, such as DP and HE, typically rely on adding noise to the data or performing encryption operations, which incur high computational costs, especially on lower-powered hardware. In contrast, the proposed method uses symmetric projection to generate low-dimensional representations, significantly optimizing both data storage and computation efficiency. Furthermore, adversarial training between the generator and discriminator allows for rapid iteration, enabling the full utilization of hardware capabilities.