## 5 Conclusions  
In this manuscript, a prefetching scheme for encrypted health data was proposed in order to boost the transmission time of health storage clouds by fetching data as needed and, in general, significantly reduce the delay of the storage cloud. More particularly, the proposed prefetch scheme follows a five-step approach, categorized in an offline and an online phase. During the offline phase, a deep learning network aiming to predict the file that should be prefetched is initially trained using a set of historical data. Afterward, a novel framework based on the influential instances concept is utilized in order to contextualize the results of the deep learning network, while in the online phase, the prefetch scheme performs a prediction of the file that may be requested in an upcoming request and prefetches it. The aforementioned prefetching scheme was evaluated through several experiments with respect to its performance, concluding that there was a substantial decrease in the transmission time when utilized. In addition, tests in order to evaluate the influence of the proposed scheme on the deep learning algorithm were also executed,confirming that the overall performance of the deep learning network was elevated when combined with the scheme. Finally, it is within our future plans to further optimize the proposed prefetching scheme in order to reduce the offline phase's learning time, starting with altering the step where the influential instances are identified by discovering them through influence functions [51] or another similar concept. In addition, we aim at completing the integration of the overall proposed storage cloud solution, including the prefetch scheme, within the InteropEHRate project [52], which aims at enabling and supporting citizens within the European Union (EU) and making up new ways to make their health data available when needed. Through the InteropEHRate project, it is within our plans to also evaluate the proposed solution in a real-world scenario, where citizens using their smartphone can back up their health data to a storage cloud such as the one presented in this research while healthcare professionals from trusted healthcare institutions can gain access to this data during emergency situations [53]. In more detail, there is already a scenario under development where the proposed prefetch engine integrated within the storage cloud will be utilized by citizens from various European Union countries (i.e., Italy, Romania, and Belgium) as well as major healthcare institutions such as the Fondazione Toscana Gabriele Monasterio (FTGM) [54] located in Pisa, Italy, the Centre Hospitalier Universitaire de Liege (CHU de Liege) [55] located in Liege, Belgium, as well as the Emergency Hospital Bagdasar-Arseni (SCUBA) [56], a clinical hospital located in Bucharest, Romania. The proposed storage cloud will be deployed and utilized by patients of the abovementioned healthcare institutions along with the healthcare professionals that cure them. Currently, the scenario includes 10 patients from each institution that will utilize the functionalities of the proposed storage cloud and healthcare professionals who, based on a simulated medical emergency, will utilize the Prefetch Engine Component. In addition, a number of encrypted medical resources will be exchanged from and to the proposed storage cloud, including data such as a citizen's allergies and intolerances, chronic conditions and current medications, as well as information about their medical history such as reports of past cardio hospitalizations (discharge reports in PDF format and other structured data). During this scenario, it will be further analyzed how the proposed solution works in a real-world use case, since currently, our work was mainly evaluated in a simulated environment, as has already been described in Section 4.