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.

ABSTRACT

Despite of its emergence and advantages in various domains, big data still suffers from major disadvantages. Timeless, scalability, and privacy are the main problems that hinder the advance of big data. Privacy preserving has become a wide search era within the scientific community. This paper covers the problem of privacy preserving over big data by combining both access control and data de-identification techniques in order to provide a powerful system. The aim of this system is to carry on all big data properties (volume, variety, velocity, veracity, and value) to ensure protection of users' identities. After many experiments and tests, our system shows high efficiency on detecting and hiding personal information while maintaining the utility of useful data. The remainder of this report is addressed in the presentation of some known works over a privacy preserving domain, the introduction of some basic concepts that are used to build our approach, the presentation of our system, and finally the display and discussion of the main results of our experiments.

Nowadays, Social networks and cloud services contain billions of users over the planet. Instagram, Facebook and other networks give the opportunity to share images. Users upload millions of pictures each day, including personal images. Another domain, which concerns medical studies, requires a highly sensitive medical images that retain personal details close to patients. Image perturbation have attracted a great deal of attention in the last few years. Many works concerning image ciphering and perturbing have been published. This paper deals with the problem of image perturbation for privacy preserving. The authors build three new systems that consist of hiding small details in pictures by rotating some pixels. Their models use two algorithms: the first one involves a simulation of the firework algorithm in which they place fireworks on selected pixels then represents sparks as rotation processes. The second system consists of a model of rotation based perturbation using iterated local search algorithm (ILS) with 2 optimization stages. Meanwhile, the third one consists of using the same principle of the previous system except by using the ILS algorithm with 3 optimization stages.

The popularization of computers, the number of electronic documents available online /offline and the explosion of electronic communication have deeply rocked the relationship between man and information. Nowadays, we are awash in a rising tide of information where the web has impacted on almost every aspect of our life. Merely, the development of automatic tools for an efficient access to this huge amount of digital information appears as a necessity. This paper deals on the unveiling of a new web information retrieval system using fireworks algorithm (FWA-IR). It is based on a random explosion of fireworks and a set of operators (displacement, mapping, mutation, and selection). Each explosion of firework is a potential solution for the need of user (query). It generates a set of sparks (documents) with two locations (relevant and irrelevant). The authors experiments were performed on the MEDLARS dataset and using the validation measures (recall, precision, f-measure, silence, noise and accuracy) by studying the sensitive parameters of this technique (initial location number, iteration number, mutation probability, fitness function, selection method, text representation, and distance measure), aimed to show the benefit derived from using such approach compared to the results of others methods existed in literature (taboo search, simulated annealing, and naïve method). Finally, a result-mining tool was achieved for the purpose to see the outcome in graphical form (3d cub and cobweb) with more realism using the functionalities of zooming and rotation.