A PRIVACY BASED DEEP LEARNING ALGORITHM FOR BIG DATA ANALYTICS
Big data, the flourishing fame in recent years, exceeds the traditional data processing methods with 5 'V' characteristics. The classification of big data is the demanding challenge to be addressed among all research issues since it provides a larger business value in any analytics environment. Classification is a mechanism that labels data enabling economical and effective performance in valuable analysis. Research has indicated that the quality of the feature may cause a backlash to the classification performance. Also squeezing the classification model with entire raw features can create a bottleneck to the classification performance. Thus there is necessity for selecting appropriate features for training the classifier. This is feasible by incorporating a feature selection method with a classification model. Hence, this research work initiate a hybrid method named HCFS Hierarchical learning for identifying appropriate feature subsets related to target class and given to classifier model to enhance the performance. The privacy need is disclosed by the veracity characteristic of the big data. The progress of science has driven every individual to mine and consume big data for analyses of business, customer, bank account, medical, etc made privacy break or intrusion in most circumstances. In addition, the data utilized for big data analytics contain restricted or copyright owned data and there exists data privacy break or intrusion. So there is an urgent need to preserve privacy with absolute principles for safeguarding the sensitive private data of every individual from intrusion for analytics. A model graph of online social network has been created for enabling analysis of the modus operandi of the application of privacy in big data environment. With the modeled graph, investigation of the privacy break or intrusion aspects in big data environment was made. This helped identification of the urgent need for individual privacy and graph protection. Hence this research work also initiates a bi level privacy preserving algorithm (PPCS with p stability) for providing privacy to individuals and for graph protection from network intrusion. The existent methods provide only a single level privacy either for individual or graph protection.