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A new approach for managing Android permissions: learning users' preferences

Abstract : Today, permissions management solutions on mobile devices employ Identity Based Access Control (IBAC) models. If this approach was suitable when people had only a few games (like Snake or Tetris) installed on their mobile phones, the current situation is different. A survey from Google in 2013 showed that, on average, french users have installed 32 applications on their Android smartphones. As a result, these users must manage hundreds of permissions to protect their privacy. Scalability of IBAC is a well-known issue and many more advanced access control models have introduced abstractions to cope with this problem. However, such models are more complex to handle by non-technical users. Thus, we present a permission management system for Android devices that (1) learns users' privacy preferences with a novel learning algorithm, (2) proposes them abstract authorization rules, and (3) provides advanced features to manage these high-level rules. Our learning algorithm is compared to two other well-known approaches to show its efficiency. Finally, we prove this whole approach is more efficient than current permission management system by comparing it to Privacy Guard Manager.
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Submitted on : Tuesday, November 6, 2018 - 4:31:48 PM
Last modification on : Tuesday, September 8, 2020 - 10:36:11 AM
Long-term archiving on: : Thursday, February 7, 2019 - 4:23:37 PM


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Arnaud Oglaza, Romain Laborde, Pascale Zaraté, Abdelmalek Benzekri, François Barrère. A new approach for managing Android permissions: learning users' preferences. EURASIP Journal on Information Security, Hindawi/SpringerOpen, 2017, 2017 (13), pp.1-16. ⟨10.1186/s13635-017-0065-4⟩. ⟨hal-01914092⟩



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