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In the world various methods are being adopted to create a systematic identification for their citizens. In year 2009, UIDAI a government body of India initiated a 12-digit number called Aadhar number, which is generated out of biometric and demographic fusion of an individual and their identity. In order to ensure a highest level of data protection as well as privacy preservation of the Aadhar card propagation through a network, it requires an efficient model of security that becomes synchronous with the cloud infrastructure. This paper initially investigates the existing approaches and their limitations towards the Aadhar card security proposes a security model namely ECrypto-AaDhaar based on the cryptography approach synchronous to the cloud architecture. The privacy preservation of the AAdhar card associative demographic and biometric information is performed considering a statistical crypto-blocking operation prior propagating it through the network in the context of cloud infrastructure. The study later also presented an experimental analysis to demonstrate the performance of ECrypto-AaDhaar technique from a time complexity perspective.

Lung cancer health care community depends on lung cancer Computer Aided Detection system to draw useful lung cancer details from Computed Tomography lung images. Nodules growth rate indicates the severity of the disease, which can be periodically radiologist analyzed by nodule segmentation and classification. Main challenges in analyzing nodules growth rate are lung nodules of different type requires special methods for segmentation, their irregular shape, and boundary. In this paper, automatic three-phase framework for lung nodules and nodules of ground glass opacity detection followed by classification is proposed. In this work, nodule segmentation framework uses proposed automatic region growing algorithm that selects set of black pixels as seed points automatically from output binary image for lung parenchyma segmentation followed by artifacts removal to reduce disease search space. Nodules are segmented based on nodule candidates center pixels identification and intensity feature of lung nodule candidates. Segmented nodules are classified using SVM classifier and classification results are compared with other considered classifiers KNN, boosting and decision tree. In the evaluation step, it was found that SVM classifier’s performance is outstanding compared to other considered classifiers in this work. Complete automation in nodule detection within very less time is the key feature of the proposed method. CT images are taken from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database to evaluate the performance of proposed work. An accuracy of 98% (45/46) with less computational time is achieved. The experimental results demonstrated that the proposed method achieve efficient and accurate segmentation of lung nodules and ground glass opacity nodules with less computation time.

In the recent era, the research interest has increased among different computer and communication societies towards microarray gene expression detection and profiling. Despite of having a wide range of applications, the more emphasize has been kept towards cancer and its sub-type classifications. It has been seen in the past that the existing data mining approaches impose more cost of computation during pattern discovery and correlation establishment. Thereby, it is needed to address this shortcoming to strengthen the reliable cancer detection and classification process cost-effectively. An efficient machine learning tool has a better scope of optimization towards handling margin and error factors. Addressing this open research issue, the current study has come up with a novel method namely Optimal Framework for Microarray Data Classification (OFDMC) which incorporates Eigenvector decomposition to perform dimension reduction of gene expression data without compromising the complexity and accuracy aspects. The study also validates the performance of the proposed system by introducing a numerical analysis.

As the technology is improving, today’s era is focusing towards big data. In that sense, various organizations are demanding an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented.