Bendechache, Malika ORCID: 0000-0003-0069-1860, Lohar, Pintu ORCID: 0000-0002-5328-1585, Xie, Guodong ORCID: 0000-0003-0037-8495, Brennan, Rob ORCID: 0000-0001-8236-362X, Trestian, Ramona ORCID: 0000-0003-3315-3081, Celeste, Edoardo ORCID: 0000-0003-1984-4142, Kapanova, Kristina, Jayasekera, Evgeniia and Tal, Irina ORCID: 0000-0001-9656-668X (2021) Public attitudes towards privacy in COVID-19 times in the Republic of Ireland: a pilot study. Information Security Journal, 30 (5). pp. 281-293. ISSN 1939-3555
Abstract
This research focuses on designing methods aimed at assessing Irish public attitudes regarding privacy in COVID-19 times and their influence on the adoption of COVID-19 spread control technology such as the COVID tracker app. The success of such technologies is dependent on their adoption rate and privacy concerns may be a factor delaying or preventing thus adoption. An online questionnaire was built to collect: demographic data, participant's general privacy profile using the Privacy Segmentation Index (PSI) which classifies individuals into 3 groups (privacy fundamentalists, pragmatists, and unconcerned), and the attitudes toward privacy in COVID-19 times. The questionnaire was shared via websites and social networks. The data was collected between 27/08/2020 to 27/9/2020. We received and analysed 258 responses. The initial pilot study found that almost 73% of the respondents were pragmatists or unconcerned about privacy when it came to sharing their private data. Comparable results were obtained with other privacy studies that have employed PSI. The pilot indicates a huge increase, from 12% pre-pandemic to 61% during the pandemic, of people willing to share their data. The questionnaire developed following this study is further used in a national survey on privacy in COVID-19 times.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Uncontrolled Keywords: | COVID-19; privacy; pandemic; attitude; information; data |
Subjects: | Computer Science > Artificial intelligence Computer Science > Machine learning |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Law and Government Research Initiatives and Centres > Lero: The Irish Software Engineering Research Centre Research Initiatives and Centres > ADAPT |
Publisher: | Taylor & Francis |
Official URL: | https://dx.doi.org/10.1080/19393555.2021.1956650 |
Copyright Information: | © 2021 Taylor & Francis. |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | Science Foundation Ireland through COVID Rapid Response programme grant number 20/COV/0229 and through the grant 13/RC/2094 co-funded under the European Regional Development Fund through the Southern and Eastern Regional Operational Programme to Lero -, ADAPT Centre for Digital Content Technology (www.adaptcentre.ie) [grant number 13/RC/2106]. |
ID Code: | 26058 |
Deposited On: | 08 Sep 2021 08:45 by Malika Bendechache . Last Modified 19 Sep 2023 08:46 |
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