Abstract
An Android smartphone contains built-in and externally downloaded applications that are used for entertainment, finance, navigation, communication, health and fitness, and so on. The behaviour of granting permissions requested by apps might expose the Android smartphone user to privacy risks. The existing works lack a formalized mathematical model that can quantify user and system applications risks. No multifaceted data collector tool can also be used to monitor the collection of user data and the risk posed by each application. A benchmark of the risk level that alerts the user and distinguishes between acceptable and unacceptable risk levels in Android smartphone user does not exist. Hence, to address privacy risk, a formalized privacy model called PRiMo that uses a tree structure and calculus knowledge is proposed. An App-sensor Mobile Data Collector (AMoDaC) is developed and implemented in real life to analyse user data accessed by mobile applications through the permissions granted and the risks involved. A benchmark is proposed by comparing the proposed PRiMo outcome with the existing available testing metrics. The results show that Tools & Utility/Productivity applications posed the highest risk as compared to other categories of applications. Furthermore, 29 users faced low and acceptable risk, while two users faced medium risk. According to the benchmark proposed, users who faced risks below 25% are considered as safe. The effectiveness and accuracy of the proposed work is 96.8%.
Highlights
The Internet of Things (IoT) is embedded deeply in various domains, such as mobile services, smart homes, enterprise services, smart environments, futuristic, personal and social applications, transportation and logistics, healthcare and utilities [1]
The research described in this article quantifies the risk of each application, the risk posed by each category of application, and the privacy exposure level of a user in an Android smartphone environment based on their usage behaviour
The user data sizes vs. users analysis are important to portray the amount of data collected by the applications in the whole usage of the Android smartphone because the high collection of user data might lead to data leakage and privacy breaches
Summary
The Internet of Things (IoT) is embedded deeply in various domains, such as mobile services, smart homes, enterprise services, smart environments, futuristic, personal and social applications, transportation and logistics, healthcare and utilities [1]. As the growth of IoT continues, the chances for users’ privacy to be exploited maliciously increase. 3.6 billion smartphone users in 2020 and this number is expected to increase to 3.8 billion in the coming year [2]. Smartphone usage is no longer limited to completing important tasks, it is used for other purposes such as entertainment, finance, navigation, communication, health and fitness, and so on. An Android smartphone contains built-in and externally downloaded applications. As of June 2020, 2.96 million applications were available in the Google Play Store [3]
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