Hadoop framework integrated hybrid optimization algorithm for privacy preserved clustering mechanism

Abstract

Big data analysis has gained immense attention throughout classical techniques, which connect in mining the hidden samples from huge data. To relieve computational complexity, the clustering technique is adapted as an imperative part. A novel model is devised for privacy preserved clustering of data with MapReduce framework. The aim is to devise an optimization technique for privacy preservation. The input data is acquired from various distributed sources. The data is further partitioned and fed to MapReduce framework, which consist of mapper and reducer. The mappers perform privacy preservation by encrypting the data with several functionalities, like encryption, Kronecker product and secret key. Here, the secret key generation is performed using proposed Chimp Grey Wolf Optimization (ChGWO) algorithm. The proposed ChGWO is developed by combining Chimp Optimization algorithm (ChOA), and Grey Wolf Optimizer (GWO). The fitness is newly developed considering utility and privacy. The privacy is Jaro Winkler similarity and utility is accuracy. Finally, the data clustering is carried out with the Deep Fuzzy Clustering (DFC). The proposed ChGWO offered enhanced efficiency with highest utility of 92.5%, highest privacy of 91.5% and highest random coefficient 65.9%.