Permission Analysis of Health and Fitness Apps in IoT Programming Frameworks

Publication Type:
Conference Proceeding
Citation:
Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, 2018, pp. 533 - 538
Issue Date:
2018-09-05
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© 2018 IEEE. Popular IoT programming frameworks, such as Google Fit, enable third-party developers to build apps to store and retrieve user data from a variety of data sources (e.g., wearables). The problem of overprivilege stands out due to the diversity and complexity of IoT apps, and developers rushing to release apps to meet the high demand in the IoT market. Any incorrect API usage of the IoT frameworks by third-party developers can lead to security risks, especially in health and fitness apps. Protecting sensitive user information is critically important to prevent financial and psychological harms. This paper presents PGFIT, a static permission analysis tool that precisely and efficiently identifies overprivilege issues in third-party apps built on top of a popular IoT programming framework, Google Fit. PGFIT extracts the set of requested permission scopes and the set of used data types in Google Fitenabled apps to determine whether the requested permission scopes are actually necessary. In this way, PGFIT serves as a quality assurance tool for developers and a privacy checker for app users. We used PGFIT to perform overprivilege analysis on a set of 20 Google Fit-enabled apps and with manual inspection, we found that 6 (30%) of them are overprivileged.
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