The electronic voting has emerged over time as a replacement to the paper based voting to reduce the redundancies and inconsistencies. The historical perspective presented in last two decades suggests that it has not been so successful due to the security and privacy flaws observed over time. This paper suggests a framework by using the effective hashing techniques to ensure the security of the data. The concept of block creation and block sealing are introduced in this paper. The introduction of block sealing concept helps in making the blockchain adjustable to meet the need of the polling process. The use of consortium blockchain is suggested that ensures that the blockchain is owned by a governing body (e.g. election commission) and no unauthorized access can be made from outside. The framework proposed in this paper discusses the effectiveness of the polling process, hashing algorithms utility, block creation and sealing, data accumulation, and result declaration by using the adjustable blockchain method. This research claims to apprehend the security and data management challenges in blockchain and provides an improved manifestation of the electronic voting process.

A number of techniques for securing plaintext, images and video frames have been developed in cryptography using jointly DNA computing and Chaos Theory. With the advancement of DNA/quantum computing, the threats of security breaches to information have an increasing possibility. In this paper, we propose a symmetric encryption algorithm for color images by extending the current encryption/decryption techniques. Our encryption algorithm is based on three chaotic systems (PWLCM, Lorenz and 4D Lorenz-type), a Secure Hash Algorithm, a scrambler, a chaotic generator and DNA sequence based Linear Feedback Shift Register. We introduce multilevel security to increase the degree of diffusion and confusion. Through experiments, we present security analysis for key irreproducibility and sensitivity, Gray Level Co-occurrence Matrix based analysis, maximum deviation, irregular deviation, entropy, histogram, variance and correlation, number of pixel change rate, unified average cipher intensity, known/chosen-plaintext attacks, mean absolute error, robustness against noises of various types using PSNR and occlusion attacks. It is demonstrated that mostly our proposed encryption algorithm has enhanced performance as compared to contemporary works in information security, while comparable in other cases.

The rapid growth of Information and Communication Technologies (ICT)—specifically, the Internet—has given emergence to e-learning. Resultantly, web-based e-learning systems are being increasingly developed to enhance the learning process. However, the utilization of such systems is low, mainly owing to poor quality content and overall design problems. To improve usage, it is imperative to identify the factors with the most significant impact on the quality of these systems so that the e-learning industry keeps these factors in consideration while developing e-learning systems. This study focused on the identification and prioritization of factors related to the design quality of e-learning systems through a hierarchical quality model. Thus, firstly, an extensive literature review was conducted to identify the factors that most affect the quality of web-based e-learning systems. Secondly, among the identified factors, only those with the most significant effect were considered. To identify the most important quality criteria, a survey was conducted. An instrument was deployed among 157 subjects, including e-learning designers, developers, students, teachers, and educational administrators. Finally, a second instrument was distributed among 51 participants to make a pairwise comparison among the criteria and rank them according to their relative importance. The identified and prioritized factors were classified into four main categories. Among these four factors, content was identified as the most important factor, whereas design was found to be the least important factor.

Software Requirements Engineering has paved its roots in both industry and academia, as today’s complex systems are programmed to provide efficient user-centric functionalities. This also refers to the emergence of challenges in Requirements Elicitation techniques, approaches and tools while performing them. Particularly, in the area of Requirements Engineering for software development, a number of techniques and approaches have been observed in literature but for mobile application development, which is different from the traditional software development, has not been discussed much in past studies. Short development cycle, device limitations and less development time for mobile application development are some of the issues to which there is no ‘silver bullet’ available. Therefore, the Requirement Analysts are in dire need of defined guidelines for Requirement Elicitation in mobile application development. With this study, we aim to provide detailed overview of Requirements Elicitation techniques and its challenges. We have conducted a systematic literature review by surveying 4507 initial and 36 primary studies. A comprehensive set of 22 elicitation techniques were measured based on quality assessment criteria including time and cost factors, resource effectiveness and domain understanding. Furthermore, challenges in Requirements Elicitation were also grouped into 8 different categories based on their applicability. Our study effectively contributes in highlighting Software Requirements Elicitation Techniques and its challenges in mobile application development.

In data publishing, privacy and utility are essential for data owners and users respectively, which cannot coexist well. This incompatibility puts the data privacy researchers under an obligation to find newer and reliable privacy preserving tradeofftechniques. Data providers like many public and private organizations (e.g. hospitals and banks) publish microdata of individuals for various research purposes. Publishing microdata may compromise the privacy of individuals. To prevent the privacy of individuals, data must be published after removing personal identifiers like name and social security numbers. Removal of the personal identifiers appears as not enough to protect the privacy of individuals. Κ-anonymity model is used to publish microdata by preserving the individual’s privacy through generalization. There exist many state-of-the-arts generalizationbased techniques, which deal with pre-defined attacks like background knowledge attack, similarity attack, probability attack and so on. However, existing generalization-based techniques compromise the data utility while ensuring privacy. It is an open question to find an efficient technique that is able to set a trade-off between privacy and utility. In this paper, we discussed existing generalization hierarchies and their limitations in detail. We have also proposed three new generalization techniques including Conventional Generalization Hierarchies (CGH), Divisors Based Generalization Hierarchies (DBGH) and Cardinality-Based Generalization Hierarchies (CBGH). Extensive experiments on the real-world dataset acknowledges that our technique outperforms among the existing techniques in terms of better utility.