Reputation-based Trust Systems Research Articles

Genetically Modified Ant Colony Optimization based Trust Evaluation in Cloud Computing

Objectives: Cloud computing is a virtualized and scalable platform which helps the users to reduce the cost incurred for setting up and maintaining an IT infrastructure. Despite the various benefits offered by cloud, it faces stringent challenges with respect to security and trust management. Trust covers the security aspects of cloud. The proposed system focuses on selection of optimal parameters used to ensure trust value. Methods: The hybridized techniques for cloud Trust Management works with a pre defined set of parameter values. Hence the trust value computed using these optimal parameters has a great impact on the overall accuracy of the trust score. We intend to use Genetically Modified Ant Colony Optimization GM-ACO technique to identify best Trust Metric Parameters (TMPs) with respect to Cloud Service Providers and it significantly outperforms compared with existing techniques. Findings: The genetically modified ACO algorithm will reduce the complexity of calculating the trust score of many service providers in the cloud environment. Applications: Managing trust in peer-to-peer systems, social network based systems, recommendation based systems, policy based systems, reputation based trust systems, trust mining systems etc.Keywords: Ant Colony Optimization, Cloud Computing, Genetic Algorithm, Trust Evaluation, Trust Management

RLM: A General Model for Trust Representation and Aggregation

Reputation-based trust systems provide important capability in open and service-oriented computing environments. Most existing trust models fail to assess the variance of a reputation prediction. Moreover, the summation method, widely used for reputation feedback aggregation, is vulnerable to malicious feedbacks. This paper presents a general trust model, called RLM, for a more comprehensive and robust reputation evaluation. Concretely, we define a comprehensive reputation evaluation method based on two attributes: reputation value and reputation prediction variance. The reputation predication variance serves as a quality measure of the reputation value computed based on aggregation of feedbacks. For feedback aggregation, we propose the novel Kalman aggregation method, which can inherently support robust trust evaluation. To defend against malicious and coordinated feedbacks, we design the Expectation Maximization algorithm to autonomously mitigate the influence of a malicious feedback, and further apply the hypothesis test method to resist malicious feedbacks precisely. Through theoretical analysis, we demonstrate the robustness of the RLM design against adulating and defaming attacks, two popular types of feedback attacks. Our experiments show that the RLM model can effectively capture the reputation's evolution and outperform the popular summation-based trust models in terms of both accuracy and attack resilience. Concretely, under the attack of collusive malicious feedbacks, RLM offers higher robustness for the reputation prediction and a lower false positive rate for the malicious feedback detection.

Reputation-based P2P trust system

Concerning the "hot spots" problem of resource access in the enhanced-reputation system,the P2P reputation system of resource balance access mechanism was proposed,and automated trust negotiation was joined to improve the system reason mechanism of confidence and negotiation efficiency.The simulation results show that P2P reputation system solves the bottleneck problem providing services of among nodes,and the success rate of interaction between requesting and providing resource nodes has been significantly improved.

A trust-based security system for ubiquitous and pervasive computing environments

Traditionally, authentication and access control based users are the main methods used to provide security for individual computers and small networks. However, now that pervasive computing is encompassing new technologies and attracting more interest, these strategies are inadequate for meeting the special characteristics of ubiquitous and pervasive computing environments because such distributed systems lack central control and have flexible topologies. Malicious nodes are a major threat during the construction of a reliable pervasive network. A reputation-based trust system can track the behavior of nodes and thereby proceed by rewarding well-behaved nodes and punishing misbehaving ones. In this paper, we propose a security system based on trust management that involves developing a trust model, assigning credentials to nodes, updating private keys, managing the trust value of each node, and making appropriate decisions about nodes’ access rights. Through the presentation of a formal security analysis of the trust system, we verify that the stated goals are achieved and that malicious nodes can be effectively excluded from ubiquitous and pervasive computing environments.