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PerRec: A Permission Configuration Recommender System for Mobile Apps

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Book cover Algorithms and Architectures for Parallel Processing (ICA3PP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10393))

Abstract

Android operating system uses a security mechanism based on permissions to restrict mobile apps to access sensitive device resources. However, because of such disadvantages as coarse-granularity of permission management and vague permission description, the current permission-based security mechanism of Android is not sufficiently effective in practice. In addition, only a small number of users realize the importance of permission settings and mostly they cannot make a proper decision on permission settings due to lack of runtime information and professional knowledge. In this paper, we propose PerRec, a permission configuration recommender system based on trust management, which assists the mobile users to set permissions in order to enhance user privacy and device security. It is designed based on our pre-developed reputation system named TruBeRepec [1] to get the trust and reputation values of an app and further offer recommendations on how to set permissions. Based on system implementation, we evaluate the accuracy and safety of PerRec by comparing PerRec’s recommendations with the Android system default permission settings. The result shows that PerRec can provide effective permission recommendations to prevent potential security threats. We further conduct a small-scale user study to demonstrate its user acceptance.

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Acknowledgments

This work is sponsored by the National Key Research and Development Program of China (grant 2016YFB0800704), the NSFC (grants 61672410 and U1536202), the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016ZDJC-06), the 111 project (grants B08038 and B16037), and Academy of Finland (grant No. 308087).

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Correspondence to Zheng Yan .

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Cheng, Y., Yan, Z. (2017). PerRec: A Permission Configuration Recommender System for Mobile Apps. In: Ibrahim, S., Choo, KK., Yan, Z., Pedrycz, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2017. Lecture Notes in Computer Science(), vol 10393. Springer, Cham. https://doi.org/10.1007/978-3-319-65482-9_34

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  • DOI: https://doi.org/10.1007/978-3-319-65482-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65481-2

  • Online ISBN: 978-3-319-65482-9

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