Critical business data in databases is an attractive target for attack. Therefore, ensuring the confidentiality, privacy and integrity of data is a major issue for the security of database systems. High secure data in databases is protected by encryption. When the data is encrypted, query performance decreases. In our paper we propose a new mechanism to query the encrypted data beside make a tradeoff between the performance and the security. Our mechanism will work over many data-types. We implement our work as a layer above the DBMS; this makes our method compatible with any DBMS. Our method based on replacing the select conditions on the encrypted data with another condition which is faster. The new way must have no security weak that is can't show an aspect for the plain data. The results of the experiments validate our approach.
Abstract-In the traveling salesman problem (TSP) we wish to find a tour of all nodes in a weighted graph so that the total weight is minimized. The traveling salesman problem is NP-hard but has many real world applications so a good solution would be useful. In this paper, we present several modern optimization techniques to find the shortest tour through all cities (nodes). Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bacteria Foraging Optimization (BFO), and Bee Colony Optimization (BCO) are applied on several datasets of TSP with different number of cities and different representation: distances between cities, or coordinates of cities. Each optimization technique has unique behaviors which survives it against other techniques. In this paper, the results and comparative study will present for each dataset to calculate the minimum distance and plat the resultant path.
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