Secure and privacy of large data using map reduction for anonymize large-scale data sets in cloud

Abstract

A large number of cloud services require users to share private data like electronic health records for data analysis or mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacy requirements such as k-anonymity is a widely used category of privacy preserving techniques. At present, the scale of data in many cloud applications increases tremendously in accordance with the Big Data trend, thereby making it a challenge for commonly used software tools to capture, manage, and process such large-scale data within a tolerable elapsed time. As a result, it is a challenge for existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this paper propose a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the Map Reduce framework on cloud. In both phases of our approach, we deliberately design a group of innovative Map Reduce jobs to concretely accomplish the specialization computation in a highly scalable way. Experimental evaluation results demonstrate that with this approach, the scalability and efficiency of TDS can be significantly improved over existing approaches.