Homomorphic Cryptography raised as a new solution used in electronic voting systems. In this research, Fully Homomorphic encryption used to design and implement an e-voting system. The purpose of the study is to examine the applicability of Fully Homomorphic encryption in real systems and to evaluate the performance of fully homomorphic encryption in e-voting systems. Most of homomorphic cryptography e-voting systems based on additive or multiplicative homomorphic encryption. In this research, fully homomorphic encryption used to provide both operations additive and multiplication, which ease the demonstration of none interactive zero-knowledge proof NIZKP. The proposed e-voting system achieved most of the important security issues of the internet-voting systems such as eligibility, privacy, accuracy, verifiability, fairness, and others. One of the most important properties of the implemented internet voting system its applicability to work on cloud infrastructure, while preserving its security characteristics. The implementation is done using homomorphic encryption library HELib. Addition and multiplication properties of fully homomorphic encryption were used to verify the correctness of vote structure as a NIZKP, and for calculating the results of the voting process in an encrypted way. The results show that the implemented internet voting system is secure and applicable for a large number of voters up to 10 million voters.

The increasing number of malwares has led to an increase in research work on malware analysis studying the malware behavior. The malware tries to leak sensitive information from infected devices. In this paper, we study a specific attack method, which distributes the data source and the point of data loss on different versions of the malware application. That is done using local storage by storing part or all of the vital data to be leaked in the future.
We introduce a Distributed Malware Detection Algorithm (DMDA), which is an algorithm to detect distributed malware on app versions. DMDA proposes a new way to analyze application against redistributed malware. DMDA is created to analyze the data and identify transitional loss points. We test this algorithm on a sample of Android applications published on the Google Play market containing 100 applications, where each application has two versions. The algorithm detected 150 transient data sources, 200 transient loss of data point and two leakages of data. In comparison, this dataset was checked using 56 anti-malware applications but none of them could find any malicious code.