Deguang Kong
ABSTRACT
To improve mobile application (App for short) user experience, it is very important to inform the users about the apps' privacy risk levels. To address the challenge of incorporating the heterogeneous feature indicators (such as app permissions, user review, developers' description and ads library) into the risk ranking model, we formalize the app risk ranking problem as an exclusive sparse coding optimization problem by taking advantage of features from different modalities via the maximization of the feature consistency and enhancement of feature diversity. We propose an efficient iterative re-weighted method to solve the resultant optimization problem, the convergence of which can be rigorously proved. The extensive experiments demonstrate the consistent performance improvement using the real-world mobile application datasets (totally 13786 apps, 37966 descriptions, 10557681 user reviews and 200 ad libraries).
- A. Argyriou, T. Evgeniou, and M. Pontil. 2008. Convex multi-task feature learning. Machine Learning 73, 3 (2008), 243-272. Google Scholar
Digital Library
- A. Blum and T. Mitchell. 1998. Combining labeled and unlabeled data with co-training. Proc. Comp. Learning Theo. (CLT1998) pp.92-100 (1998). Google ScholarDigital Library
- Navaneeth Bodla, Jingxiao Zheng, and et al.2017. Deep Heterogeneous Feature Fusion for Template-Based Face Recognition. CoRR abs/1702.04471(2017). http://arxiv.org/abs/1702.04471Google Scholar
- Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj, Vijay Raghavan, and Raju Gottumukkala. 2018. Deep Multi-view Learning to Rank. CoRR abs/1801.10402(2018).Google Scholar
- L Cen, L Si, N Li, and H Jin. 2014. User Comment Analysis for Android apps and CSPI Detection with Comment Expansion. In SIGIR.Google Scholar
- C. Chen, Y. Li, and J. Huang. 2012. Learning with Forest Sparsity. CoRR abs/1211.4657(2012).Google Scholar
- Zhenpeng Chen, Xuan Lu, Wei Ai, Huoran Li, Qiaozhu Mei, and Xuanzhe Liu. 2018. Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users. In Proceedings of the 2018 World Wide Web Conference(WWW '18). 763-772. Google ScholarDigital Library
- W. Enck, D. Octeau, P. Mcdaniel, and S. Chaudhuri. 2011. A study of Android application security. In In Proc. USENIX Security Symposium. Google ScholarDigital Library
- M. Frank, B. Dong, A. P. Felt, and D. Song. 2012. Mining Permission Request Patterns from Android and Facebook Applications. CoRR abs/1210.2429(2012). Google ScholarDigital Library
- Jerome H. Friedman. 2000. Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics 29(2000), 1189-1232.Google ScholarCross Ref
- C. S. Gates, J. Chen, N. Li, and R. W. Proctor. 2014. Effective Risk Communication for Android Apps. IEEE Trans. Dependable Sec. Comput. 11, 3 (2014), 252-265. Google ScholarDigital Library
- Mehmet Gönen and Ethem Alpayd. 2011. Multiple Kernel Learning Algorithms. Journal of Machine Learning Research 12 (jul 2011), 2211-2268. Google ScholarDigital Library
- M. C. Grace, W. Zhou, X. Jiang, and A.R. Sadeghi. 2012. Unsafe exposure analysis of mobile in-app advertisements. In WISEC. 101-112. Google ScholarDigital Library
- David R. Hardoon and etc.2004. Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Computation 16(2004), 2639-2664. Google ScholarDigital Library
- Shifu Hou, Yanfang Ye, Yangqiu Song, and Melih Abdulhayoglu. 2017. HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network. In KDD. ACM, 1507-1515. Google ScholarDigital Library
- L. Jacob, G. Obozinski, and J.-P. Vert. 2009. Group lasso with overlap and graph lasso. In ICML. 55. Google ScholarDigital Library
- Xiao-Yuan Jing, Rui-Min Hu, Yang-Ping Zhu, Shan-Shan Wu, Chao Liang, and Jing-Yu Yang. 2014. Intra-view and Inter-view Supervised Correlation Analysis for Multi-view Feature Learning. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence(AAAI'14). 1882-1889. Google ScholarDigital Library
- Deguang Kong. 2017. Science Driven Innovations Powering Mobile Product: Cloud AI vs. Device AI Solutions on Smart Device. CoRR abs/1711.07580(2017). arxiv:1711.07580http://arxiv.org/abs/1711.07580Google Scholar
- Deguang Kong, Lei Cen, and Hongxia Jin. 2015. AUTOREB: Automatically Understanding the Review-to-Behavior Fidelity in Android Applications. In Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security(CCS '15). ACM, New York, NY, USA, 530-541. Google ScholarDigital Library
- Dawei Li, Xiaolong Wang, and Deguang Kong. 2018. DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018. 2322-2330.Google ScholarCross Ref
- Bin Liu, Deguang Kong, Lei Cen, Neil Zhenqiang Gong, Hongxia Jin, and Hui Xiong. 2015. Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining(WSDM '15). ACM, New York, NY, USA, 315-324. Google ScholarDigital Library
- J. Liu, S. Ji, and J. Ye. 2012. Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization. CoRR abs/1205.2631(2012).Google Scholar
- J. Liu and J. Ye. 2010. Moreau-Yosida Regularization for Grouped Tree Structure Learning. In NIPS. 1459-1467. Google ScholarDigital Library
- Yun Ma, Ziniu Hu, Yunxin Liu, Tao Xie, and Xuanzhe Liu. 2018. Aladdin: Automating Release of Deep-Link APIs on Android. In Proceedings of the 2018 World Wide Web Conference(WWW '18). 1469-1478. Google ScholarDigital Library
- Y. Nesterov. 2007. Gradient methods for minimizing composite objective function. ECORE Discussion Paper(2007).Google Scholar
- F. Nie, H. Huang, X. Cai, and C.H.Q. Ding. 2010. Efficient and Robust Feature Selection via Joint l2, 1-Norms Minimization. In NIPS. 1813-1821. Google ScholarDigital Library
- S.J Nocedal, J.; Wright. 2006. Numerical Optimization. Springer-Verlag, Berlin, New York.Google Scholar
- R. Pandita, X. Xiao, W. Yang, and et al.2013. WHYPER: Towards Automating Risk Assessment of Mobile Applications. In USENIX Security. 527-542. Google ScholarDigital Library
- Hao Peng, Chris Gates, Bhaskar Sarma, and et al.2012. Using Probabilistic Generative Models for Ranking Risks of Android Apps. In CCS. 241-252. Google ScholarDigital Library
- Z. Qu, V. Rastogi, X. Zhang, and et al.2014. AutoCog: Measuring the Description-to-permission Fidelity in Android Applications. In ACM CCS. 1354-1365. Google ScholarDigital Library
- Abhishek Sharma. 2012. Generalized Multiview Analysis: A Discriminative Latent Space. In CVPR. IEEE Computer Society, Washington, DC, USA, 2160-2167. http://dl.acm.org/citation.cfm?id=2354409.2355114 Google ScholarDigital Library
- R. Tibshirani. 1996. Regression shrinkage and selection via the LASSO. J. Royal. Statist. Soc B. 58 (1996), 267-288.Google ScholarCross Ref
- Nicolas Viennot, Edward Garcia, and Jason Nieh. 2014. A Measurement Study of Google Play. In ACM SIGMETRICS. ACM, 221-233. Google ScholarDigital Library
- Chang Xu, Dacheng Tao, and Chao Xu. 2013. A Survey on Multi-view Learning. CoRR abs/1304.5634(2013).Google Scholar
- Xiaowei Xue, Feiping Nie, Sen Wang, Xiaojun Chang, Bela Stantic, and Min Yao. 2017. Multi-View Correlated Feature Learning by Uncovering Shared Component. In AAAI. 2810-2816. Google ScholarDigital Library
- Zhe Xue, Guorong Li, and Qingming Huang. 2016. Joint Multi-View Representation Learning and Image Tagging. In AAAI. 1366-1372. Google ScholarDigital Library
- Haichuan Yang, Yijun Huang, Lam Tran, Ji Liu, and Shuai Huang. 2016. On Benefits of Selection Diversity via Bilevel Exclusive Sparsity. In CVPR. 5945-5954.Google Scholar
- T. Yang, R. Jin, M. Mahdavi, and S. Zhu. 2012. An Efficient Primal-Dual Prox Method for Non-Smooth Optimization. CoRR abs/1201.5283(2012).Google Scholar
- Wei Yang, Deguang Kong, Tao Xie, and Carl A. Gunter. 2017. Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps. In Proceedings of the 33rd Annual Computer Security Applications Conference(ACSAC 2017). ACM, New York, NY, USA, 288-302. Google ScholarDigital Library
- L. Yuan, J. Liu, and J. Ye. 2011. Efficient Methods for Overlapping Group Lasso. In NIPS. 352-360. Google ScholarDigital Library
- M. Yuan and M. Yuan. 2006. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B 68 (2006), 49-67.Google ScholarCross Ref
- Changqing Zhang, Ehsan Adeli, Tao Zhou, Xiaobo Chen, and Dinggang Shen. 2018. Multi-Layer Multi-View Classification for Alzheimer's Disease Diagnosis. In AAAI.Google Scholar
- Jing Zhao, Xijiong Xie, Xin Xu, and Shiliang Sun. 2017. Multi-view Learning Overview. Inf. Fusion 38(2017), 43-54. Google ScholarDigital Library
- H. Zhu, H. Xiong, Y. Ge, and E. Chen. 2014. Mobile App Recommendation with Security and Privacy Awareness. In KDD. ACM, 951-960. Google ScholarDigital Library
Recommendations
Selecting a stored data approach for mobile apps
Stored data is a critical component of any application. The stored data component of mobile applications (apps) presents special considerations. This paper examines stored data for mobile apps. It identifies three types of mobile apps and describes the ...
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 ...
Interoperability ranking for mobile applications
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalAt present, most major app marketplaces perform ranking and recommendation based on search relevance features or marketplace ``popularity'' statistics. For instance, they check similarity between app descriptions and user search queries, or rank-order ...
Comments