The Privacy Paradox-Investigating People's Attitude Towards Privacy in a Time of COVID-19

The advent of digital technologies used as a mechanism to deal with the Covid-19 global pandemic, has raised serious concerns around privacy and security issues. Despite these concerns and the potential risk of data misuse, including third party use, countries around the world have pushed the use and proliferation of contact-tracing applications. However, the success of these contact-tracing applications relies on their adoption and use. A well known phenomenon referred to as privacy paradox is defined as the discrepancy between the expressed privacy concern and the actual behaviour of users when it comes to protect their privacy. In this context, this paper presents a study investigating the privacy paradox in the context of a global pandemic. A national survey has been conducted and the data is analysed to examine people's privacy risk perception. The results show inconsistencies between people's privacy concerns and their actual behaviour that is reflected in their attitude shift of sharing their mobile data during a global pandemic. The study also compiles a list of recommendations for policymakers. © 2022 IEEE.

COVID-19 Diagnosis based on Data Mining Techniques

The basic goal of data mining (DM) in the medical field is to construct a system that can accurately assess medical problems. So when the accuracy of picture detection and identification inside an image processing technique approaches that of a person, most medical images are deemed to become as accurate to healthcare experts. The use of data mining techniques may assist specialists in identifying possible inaccuracies in the classification of a variety of illnesses. As a consequence, The model implementing COVID-19 diagnosis methods is based on the data mining approaches. Some types of machine learning are included in the term DM. a convolutional neural network is a unique computer vision architecture (CNN). It's made to acquire and analyze pixel data. To discover the optimum neural network design for COVID-19 diagnosis, several key factors that influence neural network training, including learning rate & optimization method, must be considered. The major goal of this thesis would be to explain why DM is important and to figure out which type of DM is best for diagnosing COVID-19 infection quickly and accurately. Alstom to figure out how many layers and neurons should be in each layer. CNN for the most up-to-date information. The proposed techniques for COVID-19 produced impressive results, particularly in CNN, and there's a clear superiority of CNN over the other techniques;the fact that CNN relies on convolution filters produced excellent results through feature extraction due to focusing also on the intended region of the screen without the surrounding area, which resulted in a reduced number of parameters and also the speed of extraction of results with the higher resolution. The collected findings showed that the CNN-based approach has a high accuracy rate when compared to other current methods, with a 99.54% accuracy rate (with 80% training and 20% testing).