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. Unavowed data sets via generalization to satisfy certain privacy requirements such as k-anonymity is a widely used category of privacy conserving techniques. At present, the tensile of data in many cloud applications increases tremendously in accordance with the big data trend, thereby making it a challenge for frequently used software tools to capture, manage, and process such vast-scale data within a tolerable pass by time. As a result, it is a challenge for existing unavowed approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this paper, we put forward a scalable two-phase top-down specialization (tds) approach to anonymize large-scale data sets using the mapreduce framework on cloud. In both phases of our start to deal with, we consciously design a group of innovative mapreduce jobs to concretely accomplish the specialization computation in a highly scalable way.

Keywords: Data anonymization, top-down specialization, mapreduce, cloud, privacy preservation