A Multilateral Privacy Impact Analysis Method for Android Apps

Abstract

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.

Engineering Privacy in Smartphone Apps: A Technical Guideline Catalog for App Developers

“…In this context, personal data is defined as ''any information relating to an identified or identifiable natural person'', also called data subject (Art. 4 (1)). This definition is rather broad, resulting in difficult legal discussions on whether a specific data type is to be regarded as personal data or not.…”

“…Depending on the resource types, consent from users is required. Android defines four types of permissions 1 : Normal, Dangerous, Signature, and Sig-natureOrSystem. Normal level permissions allow access to resources that are considered low-risk, and they are granted during the installation of any package requesting them.…”

“…Users are often unaware of data collection practices of apps and unknowingly make a compromise against their own privacy for financial and functional benefits. This is also due to the lack of proper transparency notices and privacy indicators in app markets that would make users aware of privacy practices of apps and could assist them in an informed privacy decision-making process [1]. As the main producer of apps, developers are…”

Nudging the User with Privacy Indicator: A Study on the App Selection Behavior of the User

“…Convenience, ease of use, rich features and functionality are the established criteria for rating an app [21]. The excessive data harvesting nature of apps hardly features in the rating procedure [20]. As this negative attribute is rather kept hidden from the interface, apps' privacy invasive behavior has a very narrow scope to play an important part in a users' decision-making process while selecting an app.…”

Personal Information Classification on Aggregated Android Application’s Permissions

“…Mobile apps (Android and iOS) also cause a threat to user privacy [7][8][9][10][11][12][13][14]. App user identities are frequently compromised even with consensus [15,16].…”