抽象的

Big-Data Processing With Privacy Preserving Map-Reduce Cloud

R.Sreedhar, D.Umamaheshwari

A large number of cloud services requires 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 is challenge for existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. An introduce the scalable two-phase top-down specialization approach to anonymize large-scale data sets using the MapReduce framework on cloud. In both phases of approach is deliberately design a group of innovative MapReduce 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 top-down specialization can be improved significantly over existing approaches. An introduce the scheduling mechanism called Optimized Balanced Scheduling to apply the Anonymization. Here the OBS means individual dataset have the separate sensitive field. Every data set sensitive field and give priority for this sensitive field. Then apply Anonymization on this sensitive field only depending upon the scheduling.