Covid-19 and contact tracing apps: A review under the European legal framework

Scantamburlo, Teresa; Cortés, Àtia; Dewitte, Pierre; Eycken, Daphné Van Der; Billa, Valentina; Duysburgh, Pieter; Laenens, Willemien

doi:10.48550/arxiv.2004.14665

Cited by 4 publications

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“…Numerous conceptual papers in the system design literature have discussed the issue of privacy [ 45 - 47 ], mainly focusing on the use of tracing techniques (eg, location-based vs Bluetooth [ 48 ]) and on the use of centralized versus decentralized frameworks, urging some researchers to develop their privacy-preserving apps and frameworks [ 49 ]. Decentralized models are privacy-preserving by design; however, they are generally inefficient in responding to the needs of public health systems as they rely on individual users’ willingness to notify the network, which might never occur or might happen with delays that cannot be sustained when dealing with a highly transmissible virus such as SARS-CoV-2 [ 50 ]. Conversely, a recent simulation study showed that centralized models could be effective only when 80% of the population uses these technologies [ 51 ].…”

Section: Introductionmentioning

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

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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.

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Section: Introductionmentioning

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

show abstract

“…Compliance with the GDPR and with other EU regulations is the best way to reinforce citizens' trust and thus encourage the use of the app in the event that it is voluntary (as it should be). 110 The API launched by Apple and Google fulfils many of the privacy requirements that have been pointed out in the EU 111 and abroad: 112 (a) the app is voluntary, based on the opt-in model; (b) it is prohibited to collect or use GPS location data from the phone; (c) Bluetooth beacons and keys cannot reveal the user's identity or location; (d) users are prevented from identifying the person who has tested positive; (e) the apps based on this protocol ping at least once every 5 min as long as Bluetooth is enabled; each ping contains the device current Rolling Proximity Identifiers, 113 but these will change every 10-20 min to reduce the risk of pings being used by others to track people's location; (f) the apps will be used solely for notification of exposure to a suspicious contact in the context of the fight against the pandemic carried out by public health authorities; (g) no other use of user data is permitted, including targeted advertising. 114 Such features seem to meet the criteria of privacy by design.…”

Section: A Recommended Dct Model For Europementioning

confidence: 99%

‘I’m right behind you’: Digital contact tracing under European law

Raposo

2022

Maastricht Journal of European and Comparative Law

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The features of the coronavirus disease 2019 (COVID-19) pandemic demand a new form of contact tracing performed by digital means: digital contact tracing. However, this mechanism raises several issues in light of relevant European regulations, particularly in terms of personal data protection and privacy. The challenge is to have a digital contact tracing model that efficiently and speedily alerts people to potential infection, so they can get tested and isolated as necessary, but that also complies with European legal standards. This paper will address two main issues. First, it will analyse digital contract tracing and its different features from the perspective of data protection and privacy, seemingly the main concern in this domain. Secondly, the paper will analyse the loopholes and benefits of digital contact tracing in the European context, focusing on the tension between privacy, individual liberties and public health to address its legitimacy.

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“…Numerous conceptual papers in the system design literature have discussed the issue of privacy [45][46][47], mainly focusing on the use of tracing techniques (eg, location-based vs Bluetooth [48]) and on the use of centralized versus decentralized frameworks, urging some researchers to develop their privacy-preserving apps and frameworks [49]. Decentralized models are privacy-preserving by design; however, they are generally inefficient in responding to the needs of public health systems as they rely on individual users' willingness to notify the network, which might never occur or might happen with delays that cannot be sustained when dealing with a highly transmissible virus such as SARS-CoV-2 [50]. Conversely, a recent simulation study showed that centralized models could be effective only when 80% of the population uses these technologies [51].…”

Section: Privacy and Transparency In Data Protectionmentioning

confidence: 99%

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

Bardus,

Al Daccache,

Maalouf

et al. 2021

Preprint

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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

Covid-19 and contact tracing apps: A review under the European legal framework

Cited by 4 publications

References 0 publications

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

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

‘I’m right behind you’: Digital contact tracing under European law

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

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