Using Security Features for Cloud Computing Based on New Symmetric Key Algorithm Saeed Q. Al-Khalidi Al-Maliki
1and Fahad Alfifi 2
Yomi Gom1,Department of Computer Science, Washington Institute of Technology, USA
Abstract
Cloud computing platforms deliver critical business applications in large part because of sales commitments to security and privacy. With the help of cloud computing, large pools of resources can be connected via private or public networks to provide dynamically scalable infrastructures for application, data and file storage. Additionally, the costs of computing, application hosting, content storage and delivery can be significantly reduced. However, problems arise with cloud computing concerning data privacy, security and authenticity. Hence, our research paper presents an efficient method for providing data-storage security in cloud computing using a new simple symmetric key algorithm. This algorithm includes such important security services as key generation, encryption and decryption that are provided in cloud computing systems. The main scope of this paper is to solve the security issues in both cloud providers and cloud consumers using new cryptography methods.
A N ALGORITHM FOR SOLVING LINEAR O PTIMIZATION PROBLEMS SUBJECTED TO THE INTERSECTION OF TWO FUZZY R ELATIONAL INEQUALITIES DEFINED BY FRANK FAMILY OF T-NORMS
Amin Ghodousian*Faculty of Engineering Science, College of Engineering, University of Tehran, P.O.Box 11365-4563, Tehran, Iran
Abstract
Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated. The feasible region is formed as the intersection of two inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a necessary and sufficient condition and three other necessary conditions are presented to conceptualize the feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can easily generate a feasible test problem and employ our algorithm to it.
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