A First Look at Privacy Analysis of COVID-19 Contact Tracing Mobile Applications

Azad, Muhammad Ajmal; Arshad, Junaid; Akmal, Ali; Riaz, Farhan; Abdullah, Sidrah; Imran, Muhammad Ali; Ahmad, Farhan Jalees

doi:10.48550/arxiv.2006.13354

Cited by 1 publication

(2 citation statements)

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“…Several recent research works have highlighted numerous privacy concerns on contact tracing apps such as profiling and tracking of specific users (Wen et al, 2020;Bengio et al, 2021;Leith & Farrell, 2020), de-anonymization of infected persons (Baumgärtner et al, 2020), data misuse by third parties (Azad et al, 2020) and government surveillance (Rowe, 2020;Fahey & Hino, 2020). Some studies have explored how privacy concerns have affected the willingness of citizens to adopt contact tracing apps in different countries such as Ireland, France, Australia and Belgium.…”

Section: Table 1 Research Gaps and Our Contributionsmentioning

confidence: 99%

“…Privacy researchers as well as users have expressed deep concerns regarding privacy risks resulting from the collection, processing, and sharing of personal data by these apps and the lack of suitable privacy protection measures (Baumgärtner et al, 2020;Rowe, 2020;Fahey & Hino, 2020;Georgieva et al, 2021). Profiling and tracking of specific users (Wen et al, 2020;Bengio et al, 2021;Leith & Farrell, 2020), deanonymization of infected persons leading to stigmatization (Bäumgartner et al, 2020), data misuse by third parties (Azad et al 2020) and government surveillance (Rowe, 2020;Fahey & Hino, 2020;Georgieva et al, 2021) have been identified as some of the major privacy risks associated with contact tracing apps. The success of contact tracing applications is largely dependent on mass citizen acceptance (Fox et al, 2021) and with greater level of privacy awareness, convincing citizens to install and use these applications presents a significant challenge (Fahey & Hino, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Elucidating the role of emotion in privacy-concerns: A text-Convolutional Neural Network (Text-CNN)-based tweets analysis of contact tracing apps

Mehta

Chattopadhyay

2022

AJIS

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The extant contact tracing privacy literature is yet to explore the significance of user emotions in privacy-related decision-making such as whether to use such potentially privacy-invasive apps. Using social media analytics, the present study examines users’ privacy-related emotions stimulated by privacy-related aspects of contact tracing apps. A text-Convolutional Neural Network (Text-CNN)-based emotion analysis of tweets on the Indian contact tracing app Aarogya Setu and its Singaporean counterpart TraceTogether conducted in the paper reveals that users expressed negative privacy-related emotions towards these apps indicating high levels of perceived privacy risks and the perceived lack of privacy protection. For TraceTogether, users have also exhibited positive emotions to appreciate the steps taken by the government to protect their privacy. Based on these findings, the government/data controllers can devise strategies to assuage users’ negative emotions and promote positive emotions to encourage the adoption of contact tracing apps. This work incorporates privacy related emotions as key informants about user privacy concerns within the Privacy Calculus Theory. By relying on candid user opinions available through rich but inexpensive user-generated content, the research provides a quick, reliable, and cost-effective approach to study potential app users’ emotions to gain insights into privacy concerns related to any e-governance platform.

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