DNA matching has become one of the most used biometric identification method during the last several years. DNA stores the information for creating and organizing an organism. It can be thought of as a string over the alphabets {A, C, G, T, N}, which makes four chemical components that make it up. Here, N represents an unknown nucleotide. This unknown nucleotide may be either A, or C, or G, or T. The size of each sequence is varying in the range of millions to billions of nucleotides.Compression of DNA is interesting for both practical reasons (such as reduced storage and transmission cost) and functional reasons (such as inferring structure and function from compression models). We present a new Lossless Compression algorithm; which compresses data first horizontally and then vertically. It is based on substitution and statistical methods. We claim that our algorithm achieves one of the best compression ratios for bench mark DNA sequences in comparison to other DNA sequence compression methods. General Terms DNA Sequence Compression and Identification

Biometric systems based on a single physiological or behavioral characteristic may not be able to identify a person correctly. This paper presents an efficient and reliable multimodal biometric identification system which is based on minutiae thumb, Eigen iris and DNA sequence features. In this method compressed form of Short tandem repeat (STR) part of DNA sequence, compressed thumbprint and compressed iris image (Eigen values) of a person are used for further identification of an individual. Therefore, personal identification including identical twins and dead person's cases will become easier in using this method. Our technique will correctly identify a person (living or dead) on the basis of his thumbprint, iris image and DNA sequence features. We have used thumbprint and iris images for identifying a live person. But, for identifying a dead person we have used STR part of DNA sequence. We tested our method for 100 samples of thumbprints and iris images of CASIA database and we found that our multimodal method is able to correctly identify each and every individual including identical twin on the basis of thumbprints and iris images. For identifying a dead person the compressed form of DNA sequence of that person is used.

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Background: The data is one of the prime assets in today’s world. The continuous data generation ultimately creates huge volume of data that cannot be processed or stored by a normal relational database management system. This problem is addressed by a new concept: Big Data. Apart from the size of data, security and privacy of data are the more challenging issues in Big data technology. Objective: The primary objective of the research is to identify the potential security threats of different big data computing technologies and provide a defense mechanism to mitigate the issues. Methods: To identify the security issues different existing big data systems are thoroughly analysed and observed. Security systems are completely dependent on the system architecture. This can be a single architecture (dependent on one computing technology) or multi architecture type (dependent on multiple computing technologies). The internal mechanism of different technologies is observed and how the attacks change the behavioural pattern of the systems are the main backbone of the research. Based on the behaviour of dissimilar attacks, a comprehensive defense mechanism is identified. Security and privacy challenges of mobile healthcare are also considered as a case study. Results: The complete lists of big data computing security threats in different layers of the systems are identified. Through this research the remedial measures of the different attacks are found. The security challenges of mobile healthcare technology and its predictive measurements are sorted out. The changes of big data security systems behaviour based on its architecture are of the major findings of this research. Conclusion: The integration of mobile healthcare along with Internet of Things (IoT) and blockchain computing can enhance the system level and hence security threats can be minimized.

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