Satya Ranjan Dash

Deoxyribonucleic acid (DNA) for a shared secret key cryptosystem with Diffie hellman key sharing technique

A shared secret key based symmetric cryptographic algorithm using Diffie Hellman key sharing technique has been proposed in the paper. The shared secret key is used for encryption as well as decryption of the intended plain text. But, we are not transferring the original shared secret key through the channel. We are using Diffie Hellman key sharing technique to generate the shared secret key in both the side by exchanging the public key of the sender and receiver with each other through the channel DNA Hybridization technique has been used to produce the cipher text from the DNA sequence of plain text and the shared secret key. A numerical study with the basic parametric assumption confirms that the proposed cryptosystem is very scalable, secure and robust to use in real time system.

A Symmetric Key Cryptosystem Using DNA Sequence with OTP Key

A two stage encryption algorithm based on DNA sequence has proposed in this paper. In the first stage an encryption of plain text is done by generating a random key. The plain text is again encrypted to produce the cipher text in the second stage. Moreover, this encryption algorithm is based on a symmetric key cryptography system, where we provide a shared key to encrypt as well as decrypt the intended message. To encrypt the original key two stages are maintained and sending it over a separate secure channel other than the channel through which we are transferring the cipher text. A numerical study confirms that the proposed algorithm is reliable, secure, scalable, and robust for transmitting message.

A Survey on Cloud Environment Security Risk and Remedy

Recently, Cloud computing is most powerful research area because of its flexibility and cost efficiency. The paper describes different security issues of cloud computing and the possible solution for remediation.

Usage of Fuzzy, Rough, and Soft Set Approach in Association Rule Mining

This paper is two folded. In first fold, the authors have illustrated the interplay among fuzzy, rough, and soft set theory and their way of handling vagueness. In second fold, the authors have studied their individual strengths to discover association rules. The performance of these three approaches in discovering comprehensible rules are presented.

Disease Prediction on the Basis of SNPs

In DNA and RNA, five types of nitrogenous bases are present; these are adenine (A), uracil (U), guanine (G), thymine (T), and cytosine (C). Sometimes, these arrangements are altered which is known as single nucleotide polymorphism. These polymorphisms appear due to two causes (i) mutation and (ii) disease. By classifying these two types of SNPs, we can conclude a disease-causing SNP. In this paper, we describe genetic analysis of simple and complex disease evaluation by various methods. Here, we describe Apriori algorithm, genetic algorithm, machine learning approach like support vector machine (SVM). An approach of Apriori-Gen algorithm is also discussed which evaluates statistical interaction between several SNPs to find association among them. Univariate marginal distribution algorithm (UMDA) and support vector machine (SVM) are also used to find disease identification. USVM is used not only for its redundancy feature but also it solves parameters selecting problem of SVM.

Steganography Technique to Prevent Data Loss by Using Boolean Functions

Steganalysis technique embedded the secret message, so we have to conceal the very important data. Our main objective is undetectability, robustness and capacity of the hidden data. Existing image steganography techniques used various methods to hide data in a perfect manner. We used many Boolean functions to achieve a data (image)-hiding method or image steganography without loss of any information. Also, the data can be hidden and unhidden efficiently through the Boolean functions. Our experiments and corresponding outcome deliver that it is more secured than the existing approaches of steganography.

Frameworks to Develop SLA Based Security Metrics in Cloud Environment

Cloud computing, the growing technology which most of the small as well as large organizations adopt to maintain IT as it is a very cost effective organization should consider the business risk associated with cloud computing all of which are still not resolved. The risk can be categorized in several issues like privacy, security, legal risks. To solve these types of severe risks, organization might make and develop SLA for the establishment of an agreement between the customer and the cloud providers. This chapter provides a survey on the various frameworks to develop SLA based security metrics. Various security attributes and possible threats are having also been discussed in this chapter.

A Parallel Soft Computing Model for Identifying Lost Student in an Incomplete and Imprecise Environment

The numbers of educational institutions are growing at par with the lost student rate in a country like India. When a missing student is found we need to identify the student on the strength of some common parameter like student name, his/her institution name, branch or class etc. But we never get accurate and complete information in most of the cases to identify or recognize a lost student. In such a situation, a soft computing model can be a striking choice to track a lost student on the basis of partial information. In the past we propose soft computing model for the same. This paper proposes a more optimized parallel soft computing model which takes half of the time taken by the earlier single thread model for identifying a lost student on the basis of imprecise and partial information. The system is tested meticulously on a database of 50000 records and an efficiency of 94% is obtained.

Quantitative deciphering of local mutation in protein structures of human olfactory receptors

In Human, Odors are detected by a large family of Olfactory Receptors (ORs) proteins. A sequence of Human ORs database has been proposed by D. Lancet et al. (2000, 2006) based on divergence of evolutionary model. In our earlier work, we have reckoned many important features (viz. Fractal dimension of Indicator matrix, DNA walk, Hurst exponent of 2-adic and 4-adic representations, codon compositions) of DNA sequences and their corresponding amino acid sequences of human ORs and also after making local changes we have calculated the same and found significant quantitative variations between the original and edited sequences. But it is a fact that two same sequences may function different and two different sequences may function same due their specific protein structure. So for complete understanding the effect of mutation we need to study the structure of proteins of those DNA sequences. In this article, an effort has been made to understand the effect of local mutations in the amino acid sequences as well as their tertiary protein structures of Human ORs. The significant changes are resulted through the present analysis through Fractals and standard Bioinformatics tools. Correspondence to: Satya Ranjan Dash, School of Computer Application, KIIT University, Bhubaneswar 751024, India, E-mail: sdashfca@kiit.ac.in

On Clustering of DNA Sequence of Olfactory Receptors Using Scaled Fuzzy Graph Model

Olfactory perception is the sense of smell that allows an organism to detect chemical in its environment. The first step in odor transduction is mediated by binding odorants to olfactory receptors (ORs) which belong to the heptahelical G-protein-coupled receptor (GPCR) super-family. Mammalian ORs are disposed in clusters on virtually all chromosomes. They are encoded by the largest multigene family (\(\sim \)1000 members) in the genome of mammals and Caenorhabditis elegans, whereas Drosophila contains only 60 genes. Each OR specifically recognizes a set of odorous molecules that share common molecular features. However, local mutations affect the DNA sequences of these receptors. Hence, to study the changes among affected and non-affected, we use unsupervised learning (clustering). In this paper, a scaled fuzzy graph model for clustering has been used to study the changes before and after the local mutation on DNA sequences of ORs. At the fractional dimensional level, our experimental study confirms its accuracy.