# _Applied Sciences_
## 3 Experiments and Results  
### Angiographic View Classification  
To evaluate the utility of angiographic frames after obfuscation, we formulate four experiments in which convolutional neural networks are trained to solve the angiographic view classification task:  
* \(C_{1}\)--original images are used (no obfuscation);
* \(C_{2}\)--images are obfuscated using only the VAE encoder;
* \(C_{3}\)--images are obfuscated only through intensity substitution according to a non-bijective map;
* \(C_{4}\)--images are obfuscated using both methods, as described in Section 2.3. The details regarding these experiments are presented in Section 2.4. The accuracy obtained by the DL model for each configuration on a testing subset is reported in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Score** & **Background Description** & & & \\ \hline
1 & The background is almost monochromatic. & & & \\
2 & The prominent shadows are vaguely captured. & & & \\
3 & More accurate intensities are captured, but the background is still diffused overall. & & \\
4 & The background is close to the original one in shape and pixel intensities. Some diffused & & \\
5 & The reconstructed background is more than 95\% similar to the original one. The same shapes and shadows are depicted, but might differ in pixel intensity in specific regions. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scoring guidelines concerning the background accurateness. Figure 11: Examples of reconstructed angiographies and the scores assigned concerning the background accurateness: (**1**) monochromatic background; (**2–4**) intermediate scores; (5) accurate background reconstruction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(C_{4}\) & [11] \\ \hline Test Accuracy & 97.57\% & 93.71\% & 88.57\% & 82.71\% & 96.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between DL-model performance when trained on original and obfuscated images, respectively. Figure 10: Examples of reconstructed angiographies and the scores assigned concerning the vessels’ accurateness: (**1**) no visible vessels; (**2–4**) intermediate scores; (**5**) accurate vessels reconstruction. After altering the angiographic frames using the VAE encoder, the performance drops by approximately 4%. The method based on a non-bijective map applied to pixel intensities (\(N=96\)) leads to a decrease in accuracy of 9%. Although using both techniques causes a significant performance drop compared to the model trained on original images, the accuracy value remains above 80% and may be considered satisfactory in the context of a privacy-accuracy trade-off. The purpose of these experiments is not to achieve state-of-the-art performance on obfuscated images but to compare the results when the same architecture and different obfuscation techniques are employed. The last column of the table displays the performance previously achieved on the same dataset, using a different DL model and employing the MORE [10] homomorphic encryption scheme as a privacy-preserving technique. The accuracy was identical for the encrypted and the unencrypted model, but the computational time was around 32 times larger when encrypted data was used. In the experiments C\({}_{1}\)-C\({}_{4}\) both training and inference were performed with the same runtime since the complexity of the data is not increased by the obfuscation method. Although the MORE encryption scheme provides some advantages in terms of simplicity, clarity, and practicability, when adapted for PPML its linear structure can raise security concerns [11]. By having access to a large enough number of pairings of encrypted and unencrypted data, and by formulating the key search attack as an optimization problem, this linearity may allow one to find the secret key. Furthermore, the fact that the message to be encrypted will always be found among the eigenvalues of the ciphertext matrix is a benefit in terms of utility but also represents privacy-related disadvantages.