ABSTRACT
The current Covid-19 global pandemic led to a proliferation of contact-tracing applications meant to help control and suppress the spread of the virus. However, the success of these contact-tracing apps relies on obtaining access to sensitive data stored on citizen's mobile devices. The approaches taken are different around the world. While the countries with a strong democratic and civil liberty ethos are encouraging voluntary adoption of contact-tracing apps by their citizens, other countries opted for forced mass surveillance methods that limit individual freedoms. As a result, the attempt to fight the global pandemic is actually testing people's attitudes towards privacy and government surveillance. In this context, this research introduces a pilot study examining people's privacy concerns in a time of Covid-19. The results show that people are willing to share their personal data in the interest of controlling the spread of the virus and save lives. © 2021 IEEE.
ABSTRACT
We introduce a study examining people's privacy concerns during COVID-19 and reflect on people's willingness to share their personal data in the interest of controlling the spread of the virus and saving lives.
ABSTRACT
Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide with major concerns around privacy. COVID Tracker app used in the Republic of Ireland was praised in general for the way it addressed privacy and was used as baseline for other contact tracing apps worldwide. The success of the app is dependent on the general public uptake, hence their voice and attitude is the one that really matters. This paper focuses on developing a survey and the methods aiming to examine the attitudes toward privacy during COVID-19 of the general public in the Republic of Ireland and their impact on the uptake of the COVID tracker app. Various privacy models are used and health belief model as well in this purpose. A pilot study with 286 participants show a change in attitude towards privacy during COVID-19 pandemic, with more people willing to share their data in the interest of saving lives. However, privacy attitudes are shown to have impacted the adoption of the app in Ireland. © 2021 Owner/Author.
ABSTRACT
Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide. The major concerns in relation to these apps are around privacy. Ireland was in general praised for the design of its COVID tracker app, and the transparency through which privacy issues were addressed. However, the "voice"of the Irish public was not really heard or analysed. This study aimed to analyse the Irish public sentiment towards privacy and COVID tracker app. For this purpose we have conducted sentiment analysis on Twitter data collected from public Twitter accounts from Republic of Ireland. We collected COVID-19 related tweets generated in Ireland over a period of time from January 1, 2020 up to December 31, 2020 in order to perform sentiment analysis on this data set. Moreover, the study performed sentiment analysis on the feedback received from a national survey on privacy conducted in Republic of Ireland. The findings of the study reveal a significant criticism towards the app that relate to privacy concerns, but other aspects of the app as well. The findings also reveal some positive attitude towards the fight against COVID-19, but these are not necessarily related to the technological solutions employed for this purpose. The findings of the study contributed to the formulation of useful recommendations communicated to the relevant Irish actors. © 2021 Owner/Author.
ABSTRACT
AI in My Life' project will engage 500 Dublin teenagers from disadvantaged backgrounds in a 15-week (20-hour) co-created, interactive workshop series encouraging them to reflect on their experiences in a world shaped by Artificial Intelligence (AI), personal data processing and digital transformation. Students will be empowered to evaluate the ethical and privacy implications of AI in their lives, to protect their digital privacy and to activate STEM careers and university awareness. It extends the ĝ€DCU TY' programme for innovative educational opportunities for Transition Year students from underrepresented communities in higher education. Privacy and cybersecurity researchers and public engagement professionals from the SFI Centres ADAPT1 and Lero2 will join experts from the Future of Privacy Forum3 and the INTEGRITY H20204 project to deliver the programme to the DCU Access5 22-school network. DCU Access has a mission of creating equality of access to third-level education for students from groups currently underrepresented in higher education. Each partner brings proven training activities in AI, ethics and privacy. A novel blending of material into a youth-driven narrative will be the subject of initial co-creation workshops and supported by pilot material delivery by undergraduate DCU Student Ambassadors. Train-The-Trainer workshops and a toolkit for teachers will enable delivery. The material will use a blended approach (in person and online) for delivery during COVID-19. It will also enable wider use of the material developed. An external study of programme effectiveness will report on participants': enhanced understanding of AI and its impact, improved data literacy skills in terms of their understanding of data privacy and security, empowerment to protect privacy, growth in confidence in participating in public discourse about STEM, increased propensity to consider STEM subjects at all levels, and greater capacity of teachers to facilitate STEM interventions. This paper introduces the project, presents more details about co-creation workshops that is a particular step in the proposed methodology and reports some preliminary results. © 2021 Owner/Author.
ABSTRACT
The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing Internet traffic on the video quality in an educational context is also presented. IEEE