DocumentCode :
2984189
Title :
Mining Permission Request Patterns from Android and Facebook Applications
Author :
Frank, Michael ; Ben Dong ; Felt, Adrienne Porter ; Song, Dong
Author_Institution :
Univ. of California, Berkeley, Berkeley, CA, USA
fYear :
2012
fDate :
10-13 Dec. 2012
Firstpage :
870
Lastpage :
875
Abstract :
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.
Keywords :
Boolean algebra; application program interfaces; data mining; data privacy; matrix decomposition; operating systems (computers); social networking (online); Android; Boolean matrix factorization; Facebook; application quality; high-reputation application; low-reputation application; overlapping permission clusters; permission request pattern mining; permission system; privacy-or security-relevant API call; private data; security measure; third-party application; user satisfaction; Androids; Facebook; Hardware; Humanoid robots; Malware; Smart phones; Training; Android; Facebook; Permissions; Smartphones; Unsupervised learning; pattern mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining (ICDM), 2012 IEEE 12th International Conference on
Conference_Location :
Brussels
ISSN :
1550-4786
Print_ISBN :
978-1-4673-4649-8
Type :
conf
DOI :
10.1109/ICDM.2012.86
Filename :
6413840
Link To Document :
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