A Scalable Approach for Scheduled Data Anonymization Using MapReduce on Cloud

Cloud computing is a new development of grid, parallel, and distributed computing with visualization techniques. It is changing the IT industry in a prominent way. Cloud computing has grown due to its advantages like storage capacity, resources pooling and multi-tenancy. On the other hand, the cloud is an open environment and since all the services are offered over the Internet, there is a great deal of uncertainty about security and privacy at various levels. This paper aims to Anonymizing data sets via generalization to satisfy certain privacy requirements such as 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. we propose a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud. Together with that we develop the system to deanonymize the same data within the specific scheduled time-to-live. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches.