## 6 Unbalanced PSI-CA Protocol Based on Single-Cloud Assistance
### Experimental Analysis  
#### 6.5.1 Data Storage Volume  
In the research of this paper, the experimental analysis of the unbalanced PSI-CA protocol based on single-cloud assistance revealed a key issue: when the small health app developer needs to receive a Cuckoo filter from the large medical institution, this poses a significant challenge for receivers with limited storage capacity. This challenge is magnified when facing large datasets.  
To understand this issue deeply, a series of experiments were conducted to measure the volume of the Cuckoo filter needed by the small health app developer under different data sizes. The input data size for the experiments was provided by the large medical institution, reflecting the various data volumes that might be encountered in actual application scenarios. As shown in Table 3, the paper meticulously recorded the specific sizes of Cuckoo filter under different input data volumes, revealing the intrinsic relationship between data volume and filter size. Through experiments, it was discovered that, as the data volume in the large medical institution increased, the storage burden on the small health app developer under the original protocol also increased accordingly, with the size of the Cuckoo filter directly impacted by the input data volume. Especially in the context of the large medical institution containing extensive patient data, this storage pressure is particularly evident.  
Therefore, based on the above analysis and experimental results, it is clear that when the data volume in the large medical institution is excessively large, in other words, when the number of users reaches a certain level, the feasibility of a simple unassisted unbalanced PSI-CA protocol based on Cuckoo filter significantly decreases. This is because the unbalanced PSI-CA protocol based on Cuckoo filter requires small health app developers to directly receive and process a massive Cuckoo filter, which poses a significant challenge for small health app developers with limited storage resources, particularly mobile devices. Small health app developer devices often do not have enough storage space to accommodate these large-volume filter data, let alone process these data to complete PSI-CA operations.  
In this context, the introduction of a cloud server scheme shows its unique advantages. By transferring the storage of the filter to the cloud server, the burden on the small health app developer is greatly reduced. By this means, even in situations with a massive number of users and large data volumes, the scheme can still maintain efficient operations and ensure the smooth completion of PSI-CA operations.  
In summary, through experimental and theoretical analysis, this section concludes that, in scenarios with large-scale users and massive data volumes, the introduction of a cloud server scheme is more feasible and efficient than the unbalanced PSI-CA protocol based on Cuckoo filter.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Set Count** & **Size of Cuckoo Filter (MB)** \\ \hline
215 & 0.535 \\
217 & 2.363 \\
220 & 21.678 \\
222 & 93.645 \\
223 & 194.436 \\
224 & 403.201 \\
227 & 3571.206 \\
228 & 7372.835 \\
229 & 15,206.421 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Size of Cuckoo filter at different data volumes.