Vaccine hesitancy is a limiting factor in global efforts to contain the current pandemic, wreaking havoc on public health. As today's students are tomorrow's doctors, it is critical to understand their attitudes toward the COVID-19 vaccine. To our knowledge, this study was the first national one to look into the attitudes of Algerian medical students toward the SARS-CoV-2 vaccine using an electronic convenience survey. 383 medical students from five Algerian universities were included, with a mean age of 21.02. 85.37% (n=327) of respondents had not taken the COVID-19 vaccine yet and were divided into three groups; the vaccine acceptance group (n=175, 53.51%), the vaccine-hesitant group (n=75, 22.93%), and the vaccine refusal group (n=77, 23.54%). Gender, age, education level, university, and previous experience with COVID-19 were not significant predictors for vaccine acceptance. The confirmed barriers to the COVID-19 vaccine concern available information, effectiveness, safety, and adverse effects. This work highlights the need for an educational strategy about the safety and effectiveness of the COVID-19 vaccine. Medical students should be educated about the benefits of vaccination for themselves and their families and friends. The Vaccine acceptant students' influence should not be neglected with a possible ambassador role to hesitant and resistant students.

With the growing observed success of big data use, many challenges appeared. Timeless, scalability and privacy are the main problems that researchers attempt to figure out. Privacy preserving is now a highly active domain of research, many works and concepts had seen the light within this theme. One of these concepts is the deidentification techniques. De-identification is a specific area that consists of finding and removing sensitive information either by replacing it, encrypting it or adding a noise to it using several techniques such as cryptography and data mining. In this report, we present a new model of de-identification of textual data using a specific Immune System algorithm known as CLONALG.

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Abstract-Professional use of cloud health storage around the world implies Information-Retrieval extensions. These developments should help users find what they need among thousands or billions of enterprise documents and reports. However, extensions must offer protection against existing threats, for instance, hackers, server administrators and service providers who use people's personal data for their own purposes. Indeed, cloud servers maintain traces of user activities and queries, which compromise user security against network hackers. Even cloud servers can use those traces to adapt or personalize their platforms without users' agreements. For this purpose, we suggest implementing Private Information Retrieval (PIR) protocols to ease the retrieval task and secure it from both servers and hackers. We study the effectiveness of this solution through an evaluation of information retrieval time, recall and precision. The experimental results show that our framework ensures a reasonable and acceptable level of confidentiality for retrieval of data through cloud services.