As this article is being drafted, the SARS-CoV-2/COVID-19 pandemic is causing harm and disruption across the world. Many countries aimed at supporting their contact tracers with the use of digital contact tracing apps in order to manage and control the spread of the virus. Their idea is the automatic registration of meetings between smartphone owners for the quicker processing of infection chains. To date, there are many contact tracing apps that have already been launched and used in 2020. There has been a lot of speculations about the privacy and security aspects of these apps and their potential violation of data protection principles. Therefore, the developers of these apps are constantly criticized because of undermining users’ privacy, neglecting essential privacy and security requirements, and developing apps under time pressure without considering privacy- and security-by-design. In this study, we analyze the privacy and security performance of 28 contact tracing apps available on Android platform from various perspectives, including their code’s privileges, promises made in their privacy policies, and static and dynamic performances. Our methodology is based on the collection of various types of data concerning these 28 apps, namely permission requests, privacy policy texts, run-time resource accesses, and existing security vulnerabilities. Based on the analysis of these data, we quantify and assess the impact of these apps on users’ privacy. We aimed at providing a quick and systematic inspection of the earliest contact tracing apps that have been deployed on multiple continents. Our findings have revealed that the developers of these apps need to take more cautionary steps to ensure code quality and to address security and privacy vulnerabilities. They should more consciously follow legal requirements with respect to apps’ permission declarations, privacy principles, and privacy policy contents.

Smartphone apps have the power to monitor most of people's private lives. Apps can permeate private spaces, access and map social relationships, monitor whereabouts and chart people's activities in digital and/or real world. We are therefore interested in how much information a particular app can and intends to retrieve in a smartphone. Privacy-friendliness of smartphone apps is typically measured based on single-source analyses, which in turn, does not provide a comprehensive measurement regarding the actual privacy risks of apps. This paper presents a multi-source method for privacy analysis and data extraction transparency of Android apps. We describe how we generate several data sets derived from privacy policies, app manifestos, user reviews and actual app profiling at run time. To evaluate our method, we present results from a case study carried out on ten popular fitness and exercise apps. Our results revealed interesting differences concerning the potential privacy impact of apps, with some of the apps in the test set violating critical privacy principles. The result of the case study shows large differences that can help make relevant app choices.

This is the accepted version of a paper published in IEEE Security and Privacy. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.

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