A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization using MapReduce on Cloud

Abstract

A large number of cloud forces require users to carve up 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 massively in accordance with the Big Data trend, thereby making it a challenge for commonly used software tools to confine, manage, and process such large-scale data within a adequate elapsed time. As a result, it is a challenge for existing anonymization approaches to accomplish privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this paper, we propose a scalable two phase top-down specialization (TDS) to anonymize large-scale data sets using the MapReduce framework on cloud. In both phases of our approach, we deliberately design a group of inventive MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way. Experimental assessment results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly enhanced over existing approaches.