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Generally, clients, in any case, don’t be able to make informed decisions about which permissions are essential for their usage. Mobile phone applications today request permissions to access a large number of resources, which clients must acknowledge partially or completely during installation (e.g., on Android, on iOS) based on the application being installed in smartphones. The obtained accuracy results are promising and outperform those obtained with static analysis only. To prove the proposal’s effectiveness, we run several experiments involving different groups of participants. If this happens, the risk level has to be increased, as personal data are more exposed. This further analysis helps determining whether the collected personal data is consumed locally on the mobile device or sent out to external services. In addition, we also perform a dynamic analysis of the target app’s code. This gives the first estimation of the app’s risk level. Static analysis aims at discovering which personal data an app is collecting to determine whether the target app is asking more than required.

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This analysis adopts a hybrid strategy, exploiting static and dynamic code analyses. To support users in making more privacy-aware decisions on app usage, we propose a risk estimation approach based on an analysis of the app’s code.

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The result is that they install apps without understanding their privacy implications.

Unfortunately, understanding these practices is a complex task for average end-users. According to privacy laws, apps’ providers must inform end-users on adopted data usage practices (e.g., which data are collected and for which purpose). Indeed, mobile apps gather a massive amount of data about individuals (e.g., users’ profiles and habits) and their devices (e.g., locations), where not all are strictly needed for app execution. However, this assistance might imply a high cost in terms of privacy. Mobile apps represent essential tools in our daily routines, supporting us in almost every task.

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