A Risk Estimation Mechanism for Android Apps based on Hybrid Analysis

Abstract

Mobile apps represent essential tools in our daily routines, supporting us in almost every task. However, this assistance might imply a high cost in terms of privacy. 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. 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). Unfortunately, understanding these practices is a complex task for average end-users. The result is that they install apps without understanding their privacy implications. 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. This analysis adopts a hybrid strategy, exploiting static and dynamic code analyses. Static analysis aims at discovering which personal data an app is collecting to determine whether the target app is asking more than required. This gives the first estimation of the app’s risk level. In addition, we also perform a dynamic analysis of the target app’s code. This further analysis helps determining whether the collected personal data is consumed locally on the mobile device or sent out to external services. If this happens, the risk level has to be increased, as personal data are more exposed. To prove the proposal’s effectiveness, we run several experiments involving different groups of participants. The obtained accuracy results are promising and outperform those obtained with static analysis only.

CryptoEval: Evaluating the risk of cryptographic misuses in Android apps with data‐flow analysis

“…The data leakage modelling is relatively coarse‐grained compared with the data‐flow analysis used in this work. Our work focusses on the sensitive data operated by cryptographic APIs, which are more specialised than the personal data of [38]. Taking the information from Table 1, we summarise the differences between related approaches in Table 12.…”

“…Compared with these cryptographic misuse detection approaches, our work aims at demonstrating the risk of dependency between cryptographic misuse and related data leakage. On the other hand, the general-purpose risk estimation approach [38] takes private data collection and sharing behaviours as the metrics to measure apps' risk. The data leakage modelling is relatively coarse-grained compared with the data-flow analysis used in this work.…”

PriApp-Install: Learning User Privacy Preferences on Mobile Apps’ Installation

“…The two graphs are then compared to determine the set of dangerous APIs. Son et al [19,20] leverages on app's static analysis to model an app's behavior w.r.t data collection, on the basis of APIs, classes, functions, and constants usage. They measure an app's risk level by quantifying how much the app's behaviour diverges from the behavior of the majority of the apps in the same category.…”