Hengshu Zhu
Enhong Chen
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.
Supplemental Material
- http://developer.android.com/.Google Scholar
- http://en.wikipedia.org/wiki/cohen's_kappa.Google Scholar
- http://en.wikipedia.org/wiki/google_play.Google Scholar
- https://play.google.com/apps.Google Scholar
- K. W. Y. Au, Y. F. Zhou, Z. Huang, P. Gill, and D. Lie. Short paper: a look at smartphone permission models. In Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices, SPSM '11, pages 63--68, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- W. Enck, P. Gilbert, B.-G. Chun, L. P. Cox, J. Jung, P. McDaniel, and A. N. Sheth. Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones. In Proceedings of the 9th USENIX conference on Operating systems design and implementation, OSDI'10, pages 1--6, Berkeley, CA, USA, 2010. USENIX Association. Google ScholarDigital Library
- W. Enck, M. Ongtang, and P. McDaniel. On lightweight mobile phone application certification. In Proceedings of the 16th ACM conference on Computer and communications security, CCS '09, pages 235--245, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- A. P. Felt, E. Chin, S. Hanna, D. Song, and D. Wagner. Android permissions demystified. In Proceedings of the 18th ACM conference on Computer and communications security, CCS '11, pages 627--638, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '02, pages 133--142, New York, NY, USA, 2002. ACM. Google ScholarDigital Library
- A. Karatzoglou, L. Baltrunas, K. Church, and M. Böhmer. Climbing the app wall: enabling mobile app discovery through context-aware recommendations. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM '12, pages 2527--2530, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw. Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10, pages 939--948, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- C. Luo, H. Xiong, W. Zhou, Y. Guo, and G. Deng. Enhancing investment decisions in p2p lending: An investor composition perspective. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11, pages 292--300, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- T. Luo, H. Hao, W. Du, Y. Wang, and H. Yin. Attacks on webview in the android system. In Proceedings of the 27th Annual Computer Security Applications Conference, ACSAC '11, pages 343--352, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- H. Peng, C. Gates, B. Sarma, N. Li, Y. Qi, R. Potharaju, C. Nita-Rotaru, and I. Molloy. Using probabilistic generative models for ranking risks of android apps. In Proceedings of the 2012 ACM Conference on Computer and Communications Security, CCS '12, pages 241--252, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- K. Shi and K. Ali. Getjar mobile application recommendations with very sparse datasets. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pages 204--212, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- J. Wang and J. Zhu. Portfolio theory of information retrieval. In Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '09, pages 115--122, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- B. Yan and G. Chen. Appjoy: personalized mobile application discovery. In Proceedings of the 9th international conference on Mobile systems, applications, and services, MobiSys '11, pages 113--126, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- K. Yu, B. Zhang, H. Zhu, H. Cao, and J. Tian. Towards personalized context-aware recommendation by mining context logs through topic models. In Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I, PAKDD'12, pages 431--443, Berlin, Heidelberg, 2012. Springer-Verlag. Google ScholarDigital Library
- W. Zhang, J. Wang, B. Chen, and X. Zhao. To personalize or not: A risk management perspective. In Proceedings of the 7th ACM Conference on Recommender Systems, RecSys '13, pages 229--236, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- Y. Zhou, X. Zhang, X. Jiang, and V. W. Freeh. Taming information-stealing smartphone applications (on android). In Proceedings of the 4th international conference on Trust and trustworthy computing, TRUST'11, pages 93--107, Berlin, Heidelberg, 2011. Springer-Verlag. Google ScholarDigital Library
- H. Zhu, E. Chen, K. Yu, H. Cao, H. Xiong, and J. Tian. Mining personal context-aware preferences for mobile users. In Proceedings of the IEEE 12th International Conference on Data Mining, ICDM'12, pages 1212--1217, 2012. Google ScholarDigital Library
- H. Zhu, H. Xiong, Y. Ge, and E. Chen. Ranking fraud detection for mobile apps: A holistic view. In Proceedings of the 22Nd ACM International Conference on Conference on Information and Knowledge Management, CIKM '13, pages 619--628, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
Index Terms
- Mobile app recommendations with security and privacy awareness
Recommendations
Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference
WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data MiningRecent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending ...
An Explorative Study of the Mobile App Ecosystem from App Developers' Perspective
WWW '17: Proceedings of the 26th International Conference on World Wide WebWith the prevalence of smartphones, app markets such as Apple App Store and Google Play has become the center stage in the mobile app ecosystem, with millions of apps developed by tens of thousands of app developers in each major market. This paper ...
Structural Analysis of User Choices for Mobile App Recommendation
Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed the challenge of finding the right apps to meet the user needs. Indeed, ...
Comments