Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s13635-017-0065-4 | EURASIP J. Information Security |
Keywords | Field | DocType |
Android permission, Access control model, Recommender System | Recommender system,Permission,World Wide Web,Android (operating system),Computer security,Computer science,Mobile device,Access control,Guard (information security),Management system,Scalability | Journal |
Volume | ISSN | Citations |
2017 | 2510-523X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnaud Oglaza | 1 | 15 | 4.56 |
| Romain Laborde | 2 | 162 | 28.88 |
| Pascale Zarate | 3 | 22 | 4.78 |
| Abdelmalek Benzekri | 4 | 77 | 21.73 |
| francois barrere | 5 | 55 | 14.34 |