Efficient Association Rules Hiding Using Genetic Algorithms

In today’s world, millions of transactions are connected to online businesses, and the main challenging task is ensuring the privacy of sensitive information. Sensitive association rules hiding (SARH) is an important goal of privacy protection algorithms. Various approaches and algorithms have been developed for sensitive association rules hiding, differentiated according to their hiding performance through utility preservation, prevention of ghost rules, and computational complexity. A meta-heuristic algorithm is a good candidate to solve the problem of SARH due to its selective and parallel search behavior, avoiding local minima capability. This paper proposes simple genetic encoding for SARH. The proposed algorithm formulates an objective function that estimates the effect on nonsensitive rules and offers recursive computation to reduce them. Three benchmark datasets were used for evaluation. The results show an improvement of 81% in execution time, 23% in utility, and 5% in accuracy

Vehicular Ad Hoc Network (VANET) Connectivity Analysis of a Highway Toll Plaza

The aim of this paper was to study issues of network connectivity in vehicular ad hoc networks (VANETs) to avoid traffic congestion at a toll plaza. An analytical model was developed for highway scenarios where the traffic congestion could have the vehicles reduce their speed instead of blocking the flow of traffic. In this model, nearby vehicles must be informed when traffic congestion occurs before reaching the toll plaza so they can reduce their speed in order to avoid traffic congestion. Once they have crossed the toll plaza they can travel on at their normal speed. The road was divided into two or three sub-segments to help analyze the performance of connectivity. The proposed analytical model considered various parameters that might disturb the connectivity probability, including traveling speed, communication range of vehicles, vehicle arrival rate, and road length. The simulation results matched those of the analytical model, which showed the analytical model developed in this paper is effective

Efficient Association Rules Hiding Using Genetic Algorithms

In today’s world, millions of transactions are connected to online businesses, and the main challenging task is ensuring the privacy of sensitive information. Sensitive association rules hiding (SARH) is an important goal of privacy protection algorithms. Various approaches and algorithms have been developed for sensitive association rules hiding, differentiated according to their hiding performance through utility preservation, prevention of ghost rules, and computational complexity. A meta-heuristic algorithm is a good candidate to solve the problem of SARH due to its selective and parallel search behavior, avoiding local minima capability. This paper proposes simple genetic encoding for SARH. The proposed algorithm formulates an objective function that estimates the effect on nonsensitive rules and offers recursive computation to reduce them. Three benchmark datasets were used for evaluation. The results show an improvement of 81% in execution time, 23% in utility, and 5% in accuracy

Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center

Typically, the production data centers function with various risk factors, such as for instance the network dynamicity, topological asymmetry, and switch failures. Hence, the load-balancing schemes should consider the sensing accurate path circumstances as well as the reduction of failures. However, under dynamic traffic, current load-balancing schemes use the fixed parameter setting, resulting in suboptimal performances. Therefore, we propose a multi-level dynamic traffic load-balancing (MDTLB) protocol, which uses an adaptive approach of parameter setting. The simulation results show that the MDTLB outperforms the state-of-the-art schemes in terms of both the flow completion time and throughput in typical data center applications