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Security of Big Data is a big concern. In broad sense Big Data contains two types of data such as structured and unstructured. To provide security to unstructured data is more difficult than that of structured. In this paper we have developed an approach to give adequate security to the unstructured data by considering the types of the data and their sensitivity levels. We have reviewed the different analytics methods of Big Data, which gives us the facility to build a data node of databases of different types of data. Each type of data has been further classified to provide adequate security and enhance the overhead of the security system. To provide security to data node a security suite has been designed by incorporating different security standards and algorithms. The proper security standards or algorithms can be activated using an algorithm, which has been interfaced with the data node. We have shown that data classification with respect to sensitivity levels enhance the performance of the system.

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Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.

The widespread deployment of the Internet of Things in any smart city provides a regular flow of huge amount of data in server(s) that poses challenges for effective and efficient management to improve the quality of citizens’ life. To maintain the privacy and security of these data, a proper and secured identification and authentication process is very essential. In this paper, we propose a cluster-based identification and authentication process for the users, edge servers, and service servers, which are engaged in storing, processing, and accessing data. The proposed identification and authentication process is secured due to some codes (values), which are not possible to compute except by the concerned entities. For the proposed trust evaluation method (which actually strengthens the proposed authentication process), we consider major components and their integration in the model very carefully so that the simulation results become credible. Hence, we hope that the simulation results will be useful for the readership. As a whole, the proposed approach has potentials of being implemented in real-time applications.