A Like ELGAMAL Cryptosystem But Resistant To Post-Quantum Attacks

The Modulo 1 Factoring Problem (M1FP) is an elegant mathematical problem which could be exploited to design safe cryptographic protocols and encryption schemes that resist to post quantum attacks. The ELGAMAL encryption scheme is a well-known and efficient public key algorithm designed by Taher ELGAMAL from discrete logarithm problem. It is always highly used in Internet security and many other applications after a large number of years. However, the imminent arrival of quantum computing threatens the security of ELGAMAL cryptosystem and impose to cryptologists to prepare a resilient algorithm to quantum computer-based attacks. In this paper we will present a like-ELGAMAL cryptosystem based on the M1FP NP-hard problem. This encryption scheme is very simple but efficient and supposed to be resistant to post quantum attacks

An improved Framework for Biometric Database’s privacy

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language

New Hierarchical Routing Protocol Based on K-Means Clustering with Exploiting Free Time Slot for Wireless Sensor Networks

The operation time of LEACH protocol is divided into several frames. Each frame consists of a number of equal time slots where the selection of cluster heads and the re-establishment of clusters are periodic. Each member of the cluster exploits its own time slot to transmit the data, then it turns off its radio to reduce energy consumption. Here, the clusters generated are not uniformly distributed and the time slots of the dead member nodes are not allocated. We propose an improved version, which is based simultaneously on the integration of the clustering approach K-Means and the exploitation of the free time slot in favor of cluster head in the transmission phase. The results show that the proposed protocol allowed us to achieve increased throughput, improved reliability of the packet delivery ratio, low routing load and an effective reduction in energy consumption for low node density in WSNs

Resource optimisation in cloud computing: comparative study of algorithms applied to recommendations in a big data analysis architecture

Recommender systems (RS) have emerged as a means of providing relevant content to users, whether in social networking, health, education, or elections. Furthermore, with the rapid development of cloud computing, Big Data, and the Internet of Things (IoT), the component of all this is that elections are controlled by open and accountable, neutral, and autonomous election management bodies. The use of technology in voting procedures can make them faster, more efficient, and less susceptible to security breaches. Technology can ensure the security of every vote, better and faster automatic counting and tallying, and much greater accuracy. The election data were combined by different websites and applications. In addition, it was interpreted using many recommendation algorithms such as Machine Learning Algorithms, Vector Representation Algorithms, Latent Factor Model Algorithms, and Neighbourhood Methods and shared with the election management bodies to provide appropriate recommendations. In this paper, we conduct a comparative study of the algorithms applied in the recommendations of Big Data architectures. The results show us that the K-NN model works best with an accuracy of 96%. In addition, we provided the best recommendation system is the hybrid recommendation combined by content-based filtering and collaborative filtering uses similarities between users and items

CFA: A New Family of Hybrid CA-Based PRNGs

In this article, a new Pseudorandom Number Generator (PRNG) construction is proposed. It is based on cellular automata (CAs) and comprises other cryptographic primitives organized as blocks. Each of these blocks has a purpose and serves toward obtaining a higher level of randomness. The construction described is modular, and each of its blocks can be replaced, modified, or adapted according to the user, the application, or the level of randomness required. The authors first describe a general structure and the design principles behind each of the components. Next, a concrete example using the SHA-3 hash function, a hybrid cellular automaton, and the AES block cipher is provided. Then, the security analysis and the statistical properties for this specific instance of the scheme are presented