Privacy Preservation of Big Data Using Hadoop

Abstract

In Big data applications data collection has grown continuously, due to this it becomes expensive to manage, capture or extract and process data using existing software tools. Performing data analysis is becoming expensive with increasing large volume of data in data warehouse. Data privacy is one of the challenges in data mining with big data. To preserved the privacy of the user we need to use some method so that data privacy is preserve and at the same time increase the data utility. In existing centralized algorithms it assumes that the all data should be at centralized location for anonymization which is not possible for large scale dataset. And there was distributed algorithms which mainly focus on privacy preservation of large dataset rather than the scalability issue. In the proposed system we focus to maintain the privacy for distributed data, and also overcome the problems of M-privacy and secrecy approach with new anonymization and slicing technique. Our main goal is to publish an anonymized view of integrated data, which will be prevents the vulnerable attacks. We use MR-Cube approach which addresses the challenges of large scale cube computation with holistic measure. Slicing contains tuple partition, vertical and horizontal partition, generalization, slicing and anonymization. At the slicing is successful then anonymized data can easily access by user effectively.