# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 5 Experiments
### System Evaluation  
#### 5.2.1 Performance Analysis of Face Encryption Across Group Sizes  
We evaluated the system's performance in encrypting facial regions across different group sizes to assess scalability and efficiency. The performance metrics for the CSP component include average detection time, face encryption time, and overall execution time. For the server, we measured average processing time and image storage overhead. The results are presented in Table 5.  
The results indicate that the system's performance in encrypting facial regions is consistent across different group sizes. The average detection time ranged from 186.28 ms to 198.22 ms, with the smallest group size (Single) exhibiting the highest detection time. The average face encryption time was consistent across all group sizes, ranging from 0.44 ms to 0.64 ms. The average overall execution time was also consistent, with values ranging from 281.76 ms to 303.82 ms. The average processing time in the server was similar across group sizes, with values ranging from 311.96 ms to 340.83 ms. These results demonstrate the system's scalability and efficiency in encrypting facial regions across different group sizes.  
However, the storage requirement for images with encrypted faces is high. As shown in Table 5, the need for lossless storage leads to a substantial increase in file size, typically ranging from 10 to 12 times the size of the original image, which is encoded in JPG format. The average storage overhead was highest for the single group size, at 1245.57%, and lowest for the large group size, at 1004%.  
Figure 8: Comparison of original (top row) and encrypted (bottom row) face image pixel correlations.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**CSP**} & \multicolumn{3}{c}{**Server**} \\ \cline{2-7}
**Face Category** & **Avg. Detection** & **Avg. Face** & **Avg. Overall** & **Avg. Processing** & **Avg. Image** \\
**Time (ms)** & **Encryption Time** & **Execution Time** & **Time (ms)** & **Storage** \\  & **(ms) 1** & **(ms)** & **(ms)** & **Overhead (\%)** \\ \hline Single & 198.22 \(\pm\) 51.62 & 0.64 \(\pm\) 0.74 & 303.82 \(\pm\) 87.01 & 340.83 \(\pm\) 90.98 & 1245.57 \(\pm\) 292.26 \\ Small & 190.21 \(\pm\) 18.26 & 0.48 \(\pm\) 0.54 & 289.68 \(\pm\) 32.45 & 325.14 \(\pm\) 37.01 & 1156.74 \(\pm\) 206.04 \\ Medium & 186.28 \(\pm\) 14.96 & 0.44 \(\pm\) 0.39 & 281.76 \(\pm\) 24.64 & 314.59 \(\pm\) 28.57 & 1060.01 \(\pm\) 170.07 \\ Large & 186.44 \(\pm\) 12.42 & 0.52 \(\pm\) 0.22 & 283.03 \(\pm\) 26.14 & 311.96 \(\pm\) 27.30 & 1004 \(\pm\) 123.62 \\ \hline \hline \end{tabular}  
* The reported face encryption time represents the average total processing duration for all faces detected in a single image.  
\end{table}
Table 5: Performance analysis of face encryption across group sizes.  
In our work, we attempted to store the output image using JPG encoding. However, this encoding prevented the encrypted image from being decrypted due to compression artifacts. Even at the highest quality setting (100), JPG encoding still resulted in decryption failure. Therefore, the current version of our system uses only PNG encoding to store encrypted images, which consequently leads to high storage overhead.  
This limitation highlights a key direction for future research: optimizing encryption and storage strategies to reduce storage overhead while preserving the integrity of encrypted facial regions. Such advancements would be especially valuable for large-scale deployments where storage costs are a significant concern. Our storage evaluation, therefore, not only identifies a practical limitation of the current system but also emphasizes a critical area for further investigation in privacy-preserving image sharing.