Hierarchical PSO Clustering on MapReduce for Scalable Privacy Preservation in Big Data

Today organizations are deeply involved in the Big Data era as the amount of data has been exploding with un-predictable rate and coming from various sources. To process and analyze this massive data, privacy is a major concern together with utility of data. Thus, privacy preservation techniques which target at the balance between utility and privacy begin to be one of the recent trends for big data researchers. In this paper, we discuss a technique for big data privacy preservation by means of clustering method. Here, hierarchical particle swarm optimization (HPSO) is used for clustering similar data. To attain scalability for big data, our method is constructed on the novel cloud infrastructure, MapReduce Hadoop. The method is tested by using a novel UCI dataset and the results are compared with an existing approach.