# _Applied Sciences_
## 4 Discussion and Conclusions
### Final Conclusions  
In this paper, we present an obfuscation approach that protects the privacy of medical images while allowing for DL model training. Although obfuscation techniques have been previously researched, integrating them into medical applications might be challenging due to the strict privacy and performance requirements. Mosaicing and blurring can be used to make faces and digits unrecognizable to the human eye, as shown in [16]. According to the authors, the obfuscation methods that were evaluated preserve enough information correlated to the original images. Thus, an accurate reconstruction is possible using AI-based models. The approaches proposed in [17] assume that only a part of the images from the dataset contains sensitive information, and these will be obfuscated. However, this is not the case when training models in medical DL-based applications, where the same level of confidentiality is required for all employed data. The method also implies the risk of affecting model accuracy if too many samples need to be secured, which again is not acceptable in a medical application where both privacy and accuracy are crucial. A promising technique is presented in [18], where images are obfuscated by mixing their pixels with the pixels of another image. Other obfuscation methods were combined with the proposed technique to enhance security, and the experiments showed that the images are protected both from human perception and artificial recognition systems. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Encoding**} & \multicolumn{2}{c}{**Non-Bijective Mapping**} & \multicolumn{2}{c}{**Combined Techniques**} \\ \cline{2-7}  & **Vessels** & **Background** & **Vessels** & **Background** & **Vessels** & **Background** \\ \hline \hline \(p\)-value & 1 & 0.765 & 0.337 & 0.183 & 0.678 & 0.076 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical significance assessment regarding the reconstruction difference between LCA and RCA views. Figure 18: Comparison between LCA (**a**) and RCA (**c**) samples and their obfuscated counterparts when \(N=2\): LCA (**b**) and RCA (**d**). performance of models trained on obfuscated images varies with the level of privacy. The loss in accuracy significantly increases when methods are combined, and when privacy parameters are tuned for better security. Another aspect to consider is that the model is trained to perform the cat vs dog classification task, hence the properties of the classes are well defined, and there are many training samples available. However, since the differences between images are very subtle in specific medical imaging applications, and the available data is limited, it is unlikely that training a model on mixed images would achieve high accuracy. In the approach presented by [PERSON] et al. [19], the patient identity is protected by transforming the brain MRI into a proxy image that is sent to the server for segmentation. The altered segmentation mask is then sent to the client, who restores it to the useful version. Compared to our method, this approach differs from the initial requirements perspective, as it is designed to allow for an accurate reconstruction of the processed image. To achieve this, an identity obfuscation loss and a transformation invertibility loss based on SSIM are minimized. The mean average precision and the F1-score are used to assess the re-identification accuracy in the case of an attacker attempting to match an encoded image or segmentation against an existing database. In [20,21], generative models (GANs) were used to create visually appealing images similar to the original ones in terms of basic shape, but distinct in terms of details. Applying this method to X-ray coronary angiographies, for example, might result in synthetic angiographic frames with characteristics which are significantly different from those in the original images (possible stenoses might be excluded, vessel ramifications might be modified, etc).