Data Analytic Techniques with Hardware-Based Encryption for High-Profile Dataset M. Sharmila Begum and A. George Abstract Data analytics is the science of extracting patterns, trends, and actionable information from large sets of data. The growing nature of data in from different servers with in consistent data formats like structured, semi-structured and unstruc- tured data. Traditional IT infrastructure is simply not able to meet the demands of this new “Data Analytics” landscape. For these reasons, many enterprises are turning to the Hadoop (open source projects) as a potential solution to this unmet commercial need. As the amount of data especially unstructured data collected by organizations and enterprises explodes, Hadoop is emerging rapidly as one of the primary options for storing and performing operations on that data. The secondary problem for data analytics is security, this rapid increase in usage of Internet, drastic change in accep- tance of people using social media applications that allow users to create contents freely and amplify the already huge web volume. In today’s businesses, there are few things to keep in mind while beginning big data and analytics innovation projects. The need of secured data analytics tool is mandatory for the business world. So, in the proposed model, major intention of work is to develop the two-pass security-enabled data analytics tool. This work concentrates on two different ends of current business worlds need namely attribute-based analytical report generation and better security model for clients. This proposed work for Key generation and data analytics is the process of generating keys is used to encrypt and decrypt whatever data need to be analyzed. The work is to develop the two-pass security-enabled data analytics tool. The software and hardware keys are programmed and embedded in the kit. When the user inserts the software and hardware key, the unique key will be generated in 1024 bit key size. This will provide high level of authentication for the data to be analyzed. The data analytics part is performed with attribute-based constraints M. Sharmila Begum (B ) Department of Computer Science and Engineering, Periyar Maniammai Institute of Science and Technology, Thanjavur, Tamil Nadu, India e-mail: sharmilagaji@gmail.com A. George Department of Mathematics, Periyar Maniammai Institute of Science and Technology, Thanjavur, Tamil Nadu, India e-mail: amalanathangeorge@gmail.com © Springer Nature Singapore Pte Ltd. 2019 A. Abraham et al. (eds.), Emerging Technologies in Data Mining and Information Security, Advances in Intelligent Systems and Computing 813, https://doi.org/10.1007/978-981-13-1498-8_2 15