Implementing A Framework for Big Data Anonymity and Analytics Access Control

Abstract

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.