2014 MobileAppRecommendationswithSec

• (Zhu et al., 2014) ⇒ Hengshu Zhu, Hui Xiong, Yong Ge, and Enhong Chen. (2014). “Mobile App Recommendations with Security and Privacy Awareness.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623705

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Mobile+App+Recommendations+with+Security+and+Privacy+Awareness
• http://dl.acm.org/citation.cfm?id=2623330.2623705&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; mobile apps; recommender systems; security and privacy

Abstract

With the rapid prevalence of smart mobile devices, the number of mobile Apps available has exploded over the past few years. To facilitate the choice of mobile Apps, existing mobile App recommender systems typically recommend popular mobile Apps to mobile users. However, mobile Apps are highly varied and often poorly understood, particularly for their activities and functions related to privacy and security. Therefore, more and more mobile users are reluctant to adopt mobile Apps due to the risk of privacy invasion and other security concerns. To fill this crucial void, in this paper, we propose to develop a mobile App recommender system with privacy and security awareness. The design goal is to equip the recommender system with the functionality which allows to automatically detect and evaluate the security risk of mobile Apps. Then, the recommender system can provide App recommendations by considering both the Apps' popularity and the users' security preferences. Specifically, a mobile App can lead to security risk because insecure data access permissions have been implemented in this App. Therefore, we first develop the techniques to automatically detect the potential security risk for each mobile App by exploiting the requested permissions. Then, we propose a flexible approach based on modern portfolio theory for recommending Apps by striking a balance between the Apps' popularity and the users' security concerns, and build an App hash tree to efficiently recommend Apps. Finally, we evaluate our approach with extensive experiments on a large-scale data set collected from Google Play. The experimental results clearly validate the effectiveness of our approach.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 MobileAppRecommendationswithSec	Enhong Chen Hui Xiong Hengshu Zhu Yong Ge			Mobile App Recommendations with Security and Privacy Awareness				10.1145/2623330.2623705		2014