期刊
12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2012)
卷 -, 期 -, 页码 870-875出版社
IEEE
DOI: 10.1109/ICDM.2012.86
关键词
Unsupervised learning; pattern mining; Smart-phones; Permissions; Android; Facebook
Android and Facebook provide third-party applications with access to users' private data and the ability to perform potentially sensitive operations (e.g., post to a user's wall or place phone calls). As a security measure, these platforms restrict applications' privileges with permission systems: users must approve the permissions requested by applications before the applications can make privacy-or security-relevant API calls. However, recent studies have shown that users often do not understand permission requests and are unsure of which permissions are typical for applications. As a first step towards simplifying permission systems, we cluster a corpus of 188,389 Android applications and 27,029 Facebook applications to find patterns in permission requests. Using a method for Boolean matrix factorization to find overlapping clusters of permissions, we find that Facebook permission requests follow a clear structure that can be fitted well with only five patterns, whereas Android applications demonstrate more complex permission requests. We also find that low-reputation applications often deviate from the permission request patterns that we identified for high-reputation applications, which suggests that permission request patterns can be indicative of user satisfaction or application quality.
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