## 6 Results and Discussion
This approach ensures that clients do not need to disclose their entire raw training data to the server, but only subsets of them. Moreover, only the relevant features of subsets that are extracted using an RBM are sent to the server. Furthermore, our framework extends its privacy protection to the trained model and its parameters, safeguarding them from exposure to other clients, as the server does not need to share them with the clients. During the prediction phase, the clients encode the data for classification using the RBM model before transmitting it to the central server. The central server then executes the deep learning models, returning only the predicted output label to the client. It is evident that our model adheres to the privacy-preserving framework requirements outlined by [PERSON] et al. [8]. These requirements focus on preserving the privacy of client training data, the model and its parameters, and individual client data points during the classification phase. Furthermore, it reduces communication costs by sending a reduced subset of encodings that are smaller than the original images. Additionally, the use of RBMs for data encoding and ENN for data reduction incurs lower computational overhead compared to cryptographic techniques like Multi-Party Computation (MPC) and Homomorphic Encryption (HE). Accordingly, we posit that our work fills a notable gap in the existing literature. It offers a comprehensive solution that addresses the privacy-utility trade-off, encryption challenges, and overheads in communication and computation, while also accommodating the demands of complex deep learning models. On the other hand, our work, like most research initiatives, has some limitations that need to be acknowledged. One significant constraint is that the framework is currently designed to work only with grayscale image datasets. This limitation is a direct consequence of utilizing Restricted Boltzmann Machines in our methodology. Furthermore, our reliance on Google Colab [68] for development and evaluation limited our computational resources. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{\
\begin{tabular}{c} **The Used** \\ **Privacy Technique** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **PPDL** \\ **Frameworks** \\ \end{tabular} } & \multicolumn{3}{c}{**Privacy Metrics**} \\ \cline{3-6}  & & & **PoC** & **PoM** & **PoR** \\ \hline
[72] & MPC & SecureML & **-** & \(\surd\) & \(\surd\) \\
[73] & MPC & MiniONN & **-** & \(\surd\) & \(\surd\) \\
[74] & MPC & Chameleon & **-** & \(\surd\) & \(\surd\) \\
[54] & MPC & DeepSecure & **-** & \(\surd\) & \(\surd\) \\
[75] & MPC & ABY3 & **-** & \(\surd\) & \(\surd\) \\
[55] & MPC & SecureNN & **-** & \(\surd\) & \(\surd\) \\
[76] & HE & CryptoNets & \(\surd\) & **-** & \(\surd\) \\
[48] & HE & CryptoDL & \(\surd\) & **-** & \(\surd\) \\
[61] & TEEs & Chiron & \(\surd\) & \(\surd\) & \(\surd\) \\
[62] & TEEs & Slalom & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline
[64] & Instance Encodings & InstaHide & \(\surd\) & **-** & \(\surd\) \\ - & Instance Encodings & Our framework & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Privacy comparison [9]. The platform's inherent restrictions also prevented us from accurately assessing the actual communication time between clients and the server.