PRIVACY PRESERVATION OF DATA USING EFFICIENT GROUP COST OPTIMIZATION METHOD WITH BIG DATA CLUSTERING

Huge amount of information is being produced by different associations like clinics, banks, web based business, retail and production network, and so on by temperance of advanced innovation is a common issue. Huge amounts of information is created each moment by online life and advanced mobile phones. The voluminous information created from the different sources can be prepared and dissected to help dynamic decision making. Anyway information examination is inclined to protection violation. Presently a day's security conservation is the enormous issue on developing large information in different fields, for example, restorative, building and physical with the quickly developing system. One of the most significant difficulties in taking care of huge information is security issues. To defeat such security issues, cryptographic ideas have been utilized right now that furnish high security of huge information's with the low utilization of time for both encryption and decryption process. Different applications creating the huge information are facilitated in topographically circulated server groups, they separately gather enormous volume of information as application information just as the logs. This proposed work mostly centers on the test of moving large information from one server to other and maintaining security to the user's data. The proposed method introduces an Efficient Group Cost Optimization Method (EGCOM) using big data clustering. An effective calculation to the reduction of cost in the development of the enormous information starting with one server then onto the next for disconnected condition. This methodology utilizes the chart model for server groups in the cloud and results show that the proposed method effectively reduces the cost and improves the performance of the clustering process in handling Big data.