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  4. SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices
 
conference paper

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices

Olejnik, Katarzyna
•
Dacosta Petrocelli, Italo Ivan  
•
Soares Machado, Joana Catarina  
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2017
Proceedings of the 38th IEEE Symposium on Security and Privacy (S&P)
38th IEEE Symposium on Security and Privacy (S&P)

Permission systems are the main defense that mobile platforms, such as Android and iOS, offer to users to protect their private data from prying apps. However, due to the tension between usability and control, such systems have several limitations that often force users to overshare sensitive data. We address some of these limitations with SmarPer, an advanced permission mechanism for Android. To address the rigidity of current permission systems and their poor matching of users’ privacy preferences, SmarPer relies on contextual information and machine learning methods to predict permission decisions at runtime. Note that the goal of SmarPer is to mimic the users’ decisions, not to make privacy-preserving decisions per se. Using our SmarPer implementation, we collected 8,521 runtime permission decisions from 41 participants in real conditions. With this unique data set, we show that using an efficient Bayesian linear regression model results in a mean correct classification rate of 80% (±3%). This represents a mean relative reduction of approximately 50% in the number of incorrect decisions when compared with a user-defined static permission policy, i.e., the model used in current permission systems. SmarPer also focuses on the suboptimal trade-off between privacy and utility; instead of only “allow” or “deny” type of decisions, SmarPer also offers an “obfuscate” option where users can still obtain utility by revealing partial information to apps. We implemented obfuscation techniques in SmarPer for different data types and evaluated them during our data collection campaign. Our results show that 73% of the participants found obfuscation useful and it accounted for almost a third of the total number of decisions. In short, we are the first to show, using a large dataset of real in situ permission decisions, that it is possible to learn users’ unique decision patterns at runtime using contextual information while supporting data obfuscation; this is an important step towards automating the management of permissions in smartphones.

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Type
conference paper
DOI
10.1109/Sp.2017.25
Web of Science ID

WOS:000413081300058

Author(s)
Olejnik, Katarzyna
Dacosta Petrocelli, Italo Ivan  
Soares Machado, Joana Catarina  
Huguenin, Kévin
Khan, Mohammad Emtiyaz
Hubaux, Jean-Pierre  
Date Issued

2017

Publisher

IEEE

Publisher place

New York

Published in
Proceedings of the 38th IEEE Symposium on Security and Privacy (S&P)
ISBN of the book

978-1-5090-5533-3

Total of pages

19

Series title/Series vol.

IEEE Symposium on Security and Privacy

Subjects

mobile privacy

•

Android

•

permission systems

•

machine learning

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LDS  
Event nameEvent placeEvent date
38th IEEE Symposium on Security and Privacy (S&P)

San Jose, CA, USA

May 22-24

Available on Infoscience
March 23, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/135700
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