The Wrapper Top-Down Specialization and Bottom-up Generalization Approach for Data Anonymization Using MapReduce on Hadoop

The goal of data mining is to determine hidden useful information in large databases. Mining various patterns from transaction databases is an important problem in data mining. As the database size increment, the computation time and have need memory also gain. Base on this, adopt the MapReduce programming mode which has parallel processing ability to analysis the huge-scale network. All the experiments were taking under hadoop, arrange on a cluster which consists of commodity servers. Through experimental evaluations in different simulation conditions, the planned algorithms are shown to deliver excellent performance with respect to scalability and execution time. Focused here one more data security approach, Privacy-preserving publishing of micro data has been studied extensively in recent years. Micro data have records each of which contains information about an personage entity, such as a person, a household, or an association. Several micro data anonymization techniques have been projected. some anonymization approach, such as generalization and bucketization. we present a data provider-aware anonymization algorithm with adaptive m-privacy checking strategies to ensure high utility and m-privacy of anonymized data with good organization. Experiments on real-life datasets propose that approach achieves better or comparable utility and efficiency than existing and baseline algorithms while providing m-privacy guarantee.