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Al-Zobbi, M., Shahrestani, S., Ruan, C..  2017.  Implementing A Framework for Big Data Anonymity and Analytics Access Control. 2017 IEEE Trustcom/BigDataSE/ICESS. :873–880.

Analytics in big data is maturing and moving towards mass adoption. The emergence of analytics increases the need for innovative tools and methodologies to protect data against privacy violation. Many data anonymization methods were proposed to provide some degree of privacy protection by applying data suppression and other distortion techniques. However, currently available methods suffer from poor scalability, performance and lack of framework standardization. Current anonymization methods are unable to cope with the massive size of data processing. Some of these methods were especially proposed for MapReduce framework to operate in Big Data. However, they still operate in conventional data management approaches. Therefore, there were no remarkable gains in the performance. We introduce a framework that can operate in MapReduce environment to benefit from its advantages, as well as from those in Hadoop ecosystems. Our framework provides a granular user's access that can be tuned to different authorization levels. The proposed solution provides a fine-grained alteration based on the user's authorization level to access MapReduce domain for analytics. Using well-developed role-based access control approaches, this framework is capable of assigning roles to users and map them to relevant data attributes.

Xue, S., Zhang, L., Li, A., Li, X., Ruan, C., Huang, W..  2018.  AppDNA: App Behavior Profiling via Graph-Based Deep Learning. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1475-1483.

Better understanding of mobile applications' behaviors would lead to better malware detection/classification and better app recommendation for users. In this work, we design a framework AppDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The behavior difference between two apps can be measured by the distance between their representations. As a result, the versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorizing, plagiarism detection, etc. Based on a systematic and deep understanding of an app's behavior, we propose to perform a function-call-graph-based app profiling. We carefully design a graph-encoding method to convert a typically extremely large call-graph to a 64-dimension fix-size vector to achieve robust app profiling. Our extensive evaluations based on 86,332 benign and malicious apps demonstrate that our system performs app profiling (thus malware detection, classification, and app recommendation) to a high accuracy with extremely low computation cost: it classifies 4024 (benign/malware) apps using around 5.06 second with accuracy about 93.07%; it classifies 570 malware's family (total 21 families) using around 0.83 second with accuracy 82.3%; it classifies 9,730 apps' functionality with accuracy 33.3% for a total of 7 categories and accuracy of 88.1 % for 2 categories.