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Android Permission Recommendation Using Transitive Bayesian Inference Model

  • Bahman RashidiEmail author
  • Carol Fung
  • Anh Nguyen
  • Tam Vu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9878)

Abstract

In current Android architecture, users have to decide whether an app is safe to use or not. Technical-savvy users can make correct decisions to avoid unnecessary privacy breach. However, most users may have difficulty to make correct decisions. DroidNet is an Android permission recommendation framework based on crowdsourcing. In this framework, DroidNet runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our evaluation results demonstrate that given sufficient number of experts in the network, DroidNet can provide accurate recommendations and cover majority of app requests given a small coverage from a small set of initial experts.

Keywords

Expertise Rating Recommendation Algorithm Regular User Expert User Expertise Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bahman Rashidi
    • 1
    Email author
  • Carol Fung
    • 1
  • Anh Nguyen
    • 2
  • Tam Vu
    • 2
  1. 1.Virginia Commonwealth UniversityRichmondUSA
  2. 2.University of Colorado DenverDenverUSA

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