Market-level analysis of government-backed COVID-19 contact tracing apps

Wang, Huiyi; Wang, Liu; Wang, Haoyu

doi:10.1145/3417113.3422186

Cited by 9 publications

(6 citation statements)

References 7 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This hypothesis was backed by results obtained in the study. The current literature has widespread support for this finding of our study (González‐Cabañas et al, 2021; Kucharski et al, 2020; Rowe, 2020; Wang et al, 2020). The benefits accrued from CTA usage are not instantly or visibly evident to its users; this can lead to questioning its beneficial usage.…”

Section: Discussionsupporting

confidence: 81%

“…First, it augments the literature with additional factors explaining citizens' resistance to adopting CTAs. The current studies have probed different dimensions of the contact tracing app (e.g., Kindt et al, 2020; Maccari & Cagno, 2021; Wang et al, 2020). None of the current studies have based their research on the IRT used in the current study.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

See 1 more Smart Citation

Citizen's adoption intention toward contact tracing apps: An analysis using innovation resistance theory

Chakraborty

Siddiqui

2022

Journal of Public Affairs

View full text Add to dashboard Cite

This study explores the factors that prevent citizens from using contact tracing apps (CTA). It uses the innovation resistance theory to construct a parsimonious data analysis model. Data are collected from 440 users of CTA, and an analysis using structural equation modeling is done. Moderation analysis is performed to measure the influence of trust on the relationship between independent and dependent variables. The results reveal that privacy concerns, functional complexity, and benefit ambiguity are the main factors causing hindrance in the adoption of CTAs. The study carries implications for public authorities and technology professionals for promoting and increasing the uptake of public-oriented mass-consumption technology.

show abstract

Section: Discussionsupporting

confidence: 81%

Section: Theoretical Implicationsmentioning

confidence: 99%

Citizen's adoption intention toward contact tracing apps: An analysis using innovation resistance theory

Chakraborty

Siddiqui

2022

Journal of Public Affairs

View full text Add to dashboard Cite

show abstract

“…Although it can be efficient from a public health perspective, this approach might generate some general privacy concerns. Our findings show a negative correlation between the Permission Accumulated Risk Score and the average rating of the selected apps on the Apple App Store, which might indicate that users did not like the design or usability or did not trust these apps, expressing a lower rating [ 78 ].…”

Section: Discussionmentioning

confidence: 99%

Data Management and Privacy Policy of COVID-19 Contact-Tracing Apps: Systematic Review and Content Analysis

Bardus¹,

Daccache²,

Maalouf³

et al. 2022

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background COVID-19 digital contact-tracing apps were created to assist public health authorities in curbing the pandemic. These apps require users’ permission to access specific functions on their mobile phones, such as geolocation, Bluetooth or Wi-Fi connections, or personal data, to work correctly. As these functions have privacy repercussions, it is essential to establish how contact-tracing apps respect users’ privacy. Objective This study aimed to systematically map existing contact-tracing apps and evaluate the permissions required and their privacy policies. Specifically, we evaluated the type of permissions, the privacy policies’ readability, and the information included in them. Methods We used custom Google searches and existing lists of contact-tracing apps to identify potentially eligible apps between May 2020 and November 2021. We included contact-tracing or exposure notification apps with a Google Play webpage from which we extracted app characteristics (eg, sponsor, number of installs, and ratings). We used Exodus Privacy to systematically extract the number of permissions and classify them as dangerous or normal. We computed a Permission Accumulated Risk Score representing the threat level to the user’s privacy. We assessed the privacy policies’ readability and evaluated their content using a 13-item checklist, which generated a Privacy Transparency Index. We explored the relationships between app characteristics, Permission Accumulated Risk Score, and Privacy Transparency Index using correlations, chi-square tests, or ANOVAs. Results We identified 180 contact-tracing apps across 152 countries, states, or territories. We included 85.6% (154/180) of apps with a working Google Play page, most of which (132/154, 85.7%) had a privacy policy document. Most apps were developed by governments (116/154, 75.3%) and totaled 264.5 million installs. The average rating on Google Play was 3.5 (SD 0.7). Across the 154 apps, we identified 94 unique permissions, 18% (17/94) of which were dangerous, and 30 trackers. The average Permission Accumulated Risk Score was 22.7 (SD 17.7; range 4-74, median 16) and the average Privacy Transparency Index was 55.8 (SD 21.7; range 5-95, median 55). Overall, the privacy documents were difficult to read (median grade level 12, range 7-23); 67% (88/132) of these mentioned that the apps collected personal identifiers. The Permission Accumulated Risk Score was negatively associated with the average App Store ratings (r=−0.20; P=.03; 120/154, 77.9%) and Privacy Transparency Index (r=−0.25; P<.001; 132/154, 85.7%), suggesting that the higher the risk to one’s data, the lower the apps’ ratings and transparency index. Conclusions Many contact-tracing apps were developed covering most of the planet but with a relatively low number of installs. Privacy-preserving apps scored high in transparency and App Store ratings, suggesting that some users appreciate these apps. Nevertheless, privacy policy documents were difficult to read for an average audience. Therefore, we recommend following privacy-preserving and transparency principles to improve contact-tracing uptake while making privacy documents more readable for a wider public.

show abstract

“…Analysis of mobile apps showed sensitive data, including PII [44], is collected and shared with third parties without user consent [31]. Data collection practices were prevalent across measurements of apps in different geographic regions [49], categories [52,54], price brackets [48,22,23], and app markets [32,51,38].…”

Section: Background and Related Workmentioning

confidence: 99%

Longitudinal Analysis of Privacy Labels in the Apple App Store

G.¹,

Ali²,

Wu³

et al. 2022

Preprint

View full text Add to dashboard Cite

In December of 2020, Apple started to require app developers to annotate their apps with privacy labels that indicate what data is collected and how it is used. We collected and analyzed 36 weekly snapshots of 1.6 million apps between July 15, 2021 and March 17, 2022 to better understand the privacy label ecosystem, the categories and types of data collected, and the purposes that developers used to justify that collection. Nearly two years after privacy labels launched, only 60.5% of apps have privacy labels, increasing by an average of 0.5% per week, mostly driven by new apps rather than older apps coming into compliance. Of apps with labels, 17.3% collect data used to track users, 37.6% collect data that is linked to a user identity, and 41.9% collect data that is not linked. Only 42.1% of apps with labels indicate that they do not collect any data. We find that because many apps indicate that they do not collect any data, even apps that would seem likely to collect or link data, trusting the veracity of privacy labels is still an open question.

show abstract

Market-level analysis of government-backed COVID-19 contact tracing apps

Cited by 9 publications

References 7 publications

Citizen's adoption intention toward contact tracing apps: An analysis using innovation resistance theory

Citizen's adoption intention toward contact tracing apps: An analysis using innovation resistance theory

Data Management and Privacy Policy of COVID-19 Contact-Tracing Apps: Systematic Review and Content Analysis

Longitudinal Analysis of Privacy Labels in the Apple App Store

Contact Info

Product

Resources

About