Direct Trust Value Research Articles

PSO-Based Trust QoS Routing Algorithm for Wireless Sensor Networks

The existing routing algorithm of Wireless Sensor Networks (WSNs) pays more attention to the energy efficient. However, Quality of Service (QoS) supported and the validity of packet transmission unveil additional challenges. Based on the Particle Swarm Optimization (PSO), this paper presents a trust QoS routing algorithm for WSNs. Hierarchical structure is adopted and the trust value of the node is constructed with the direct trust value and the indirect trust value. In addition, delivery delay and packet loss are considered as main QoS metrics. Finally, under the optimization ability of PSO, the proposed algorithm tries to find the route with maximum residual energy and reliability in order to maximize network utilization as well as meets QoS constraints. Compared to typical routing algorithms in sensor networks, this new algorithm has better performance.

Network Trustworthy Assessment Based on Advanced DyTrust Model

According to the algorithm of direct trust value in DyTrust Model, a time attenuation factor was presented, and the influence of time factor was considered. At the same time, a risk function was put forward, and a new model named A-DyTrust was proposed. Based on the proportional relationship to the quality and risk of request services by node, a formula of the risk function was raised to improve the DyTrust Model. The simulation results show that A-DyTrust Model is more effective than DyTrust Model, which has better dynamic adaptive capacity, higher ability to detect malicious nodes and more effectively aggregate capacity of the information than DyTrust Model. Aim at the question of network trust rating, a method of assessment for network trustworthy based on the associative memory capability of the Hopfield Neural Network was given. The simulation results show that this method can determine the level of trust of current network accurately, then achieve the quantification of network trust.

Combining Feedback and Trust Scope Parameter: A New Model for Assessing Information Source Trust

Abstract Reliable information is incredibly essential for policy and decision makers, especially when the decision to be made is related to a crucial interest. There are various ways used to get a reliable information. One of them is by looking at the trust level of the information source. This trust level can be determined by assessing the source of information's reputation. There are some parameters used in determining the value of the source of information reputation: such as feedback, trust scope, and trust level. Generally, these parameters are used separately in accordance to the purpose of the trust assessment conducted. Feedback, for example, is usually used to get a direct trust value. It is conducted to determine reputation value by accumulating the direct trust value with the previous reputation value. Meanwhile, trust scope is usually used to assess the trust of the information source based upon the context. However, implementing trust scope or feedback method alternately will not allow combined external and internal representations. In brief, separate implementation does not provide optimum result. Therefore, this paper proposes to combine two parameters: feedback and trust scope, considering each advantages and disadvantages. The underlying reason of combining both parameters is to get a stronger and more balanced trust assessment, as the trust value will be obtained both from feedback and the source of information trust in the context.

A Trust Value Computing Approach for Web Service

Building a trust mechanism for Web service is very important. First, Bayes method is used to evaluate the interaction success probability of Web service; then direct trust value, indirect trust value, reputation value and total trust value are defined respectively; third, the sample size determination is discussed; finally, the simulation experiment results demonstrate its feasibility and effectiveness.

METrust: A mutual evaluation-based trust model for P2P networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer (P2P) network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers' evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers' malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers' own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

Designing Trust Propagation Algorithms based on Simple Multiplicative Strategy for Social Networks

In the recent years, with the huge popularity of Web based Social Networks, the trust and trust related issues become more and more important. Many trust metrics have been designed in literature for calculating propagated trust in social networks. They are either of type global or local. Global trust metrics build a global rank or trust value for the nodes or users in the network. On the other hand, local trust metrics take source node as the input and calculate the personalized trust value between that node and a destination node by trust propagation mechanisms. In this paper, we have designed three trust propagation algorithms based on simple multiplicative strategy of trust propagation by taking path length and decay of direct trust values along the trust path into consideration in calculating propagated trust. The accuracy of the algorithms in predicting propagated trust values are calculated and compared with the mole trust [7] algorithm. We have used Mean absolute Error (MAE) as our evaluation metric to measure the extent of deviation of the propagated trust values from the actual trust ratings. We have also examined whether there is any strong correlation between direct trust and propagated trust values calculated by our algorithms.

Trustworthy assurance of service interoperation in cloud environment

Cloud computing can be realized by service interoperation and its essence is to provide cloud services through network. The development of effective methods to assure the trustworthiness of service interoperation in cloud environment is a very important problem. The essence of cloud security is trust and trust management. Combining quality of service (QoS) with trust model, this paper constructs a QoS-aware and quantitative trust-model that consists of initial trust value, direct trust value, and recommendatory trust value of service, making the provision, discovery, and aggregation of cloud services trustworthy. Hence, it can assure trustworthiness of service interoperation between users and services or among services in cloud environment. At the same time, based on this model, service discovery method based on QoS-aware and quantitative trust-model (TQoS-WSD) is proposed, which makes a solid trust relationship among service requestor, service provider and service recommender, and users can find trustworthy service whose total evaluation value is higher. Compared to QoS-based service discovery (QoS-WSD) method, it is proved by the experiment for TQoS-WSD method that more accurate result of service discovery will be achieved by service requestor, while reasonable time cost is increased. Meanwhile, TQoS-WSD method strongly resists the effect of service discovery by untrustworthy QoS values and improves service invocation success-rate and thus assures trustworthiness of services interoperation.

Strategies for predicting local trust based on trust propagation in social networks

Trust plays a critical role in determining social interactions in both online and offline networks, and reduces information overload, uncertainties and risk from unreliable users. In a social network, even if two users are not directly connected, one user can still trust the other user if there exists at least one path between the two users through friendship networks. This is the result of trust propagation based on the transitivity property of trust, which is “A trusts B and B trusts C, so A will trust C”. It is important to provide a trust inference model to find reliable trust paths from a source user to an unknown target user, and to systematically combine multiple trust paths leading to a target user. We propose strategies for estimating level of trust based on Reinforcement Learning, which is particularly well suited to predict a long-term goal (i.e. indirect trust value on long-distance user) with short-term reward (i.e. direct trust value between directly connected users). In other words, we compare and evaluate how the length of available trust paths and aggregation methods affects prediction accuracy and then propose the best strategy to maximize the prediction accuracy.

An Incentive Compatible Reputation Model for P2P Networks

An important challenge regarding peer’s trust assessment in peer-to-peer (P2P) networks is how to cope with such issues as the fraudulent behaviors and the dishonest feedback behaviors from malicious peers, and the issue of inactive recommendations to others. However, these issues cannot be effectively addressed by the existing solutions. Thus, an incentive compatible reputation management model for P2P networks, named ICRM, is proposed to solve them. In ICRM, the metric of time zone is used to describe the time property of the transaction experience and the recommendation. Three other metrics such as the direct trust value, the recommendation trust value and the recommendation credibility, based on the metric of time zone are applied to express accurately the final trust level of a peer. Furthermore, the participating level is introduced as the metric to identify a peer’s activeness degree. Theoretical analysis and simulation experiments demonstrate that, ICRM can effectively suppress the malicious behaviors such as providing unreliable services, or giving dishonest feedbacks to others in the P2P networks. What’s more, it also can incent peers to offer recommendations to others more actively.

A Trust Evaluation Algorithm for Wireless Sensor Networks Based on Node Behaviors and D-S Evidence Theory

For wireless sensor networks (WSNs), many factors, such as mutual interference of wireless links, battlefield applications and nodes exposed to the environment without good physical protection, result in the sensor nodes being more vulnerable to be attacked and compromised. In order to address this network security problem, a novel trust evaluation algorithm defined as NBBTE (Node Behavioral Strategies Banding Belief Theory of the Trust Evaluation Algorithm) is proposed, which integrates the approach of nodes behavioral strategies and modified evidence theory. According to the behaviors of sensor nodes, a variety of trust factors and coefficients related to the network application are established to obtain direct and indirect trust values through calculating weighted average of trust factors. Meanwhile, the fuzzy set method is applied to form the basic input vector of evidence. On this basis, the evidence difference is calculated between the indirect and direct trust values, which link the revised D-S evidence combination rule to finally synthesize integrated trust value of nodes. The simulation results show that NBBTE can effectively identify malicious nodes and reflects the characteristic of trust value that ‘hard to acquire and easy to lose’. Furthermore, it is obvious that the proposed scheme has an outstanding advantage in terms of illustrating the real contribution of different nodes to trust evaluation.

A Reputation System with Anti-Pollution Mechanism in P2P File Sharing Systems

File pollution has become a very serious problem in peer-to-peer file sharing systems, because of which it greatly reduces the effectiveness of systems. Users downloaded pollution files not only consumed bandwidth, but were also likely to share polluted files without checking. If these polluted files carry a virus, Trojan horse, or other malicious code, the loss of users would be disastrous. There is much research done on reputation-based anti-pollution mechanisms. Peer reputation systems and object reputation systems are two representatives reputation-based anti-pollution mechanisms currently. Peer reputation systems only care about the reputation of file providers, while object reputation systems only care about the reputation of sharing files. This paper proposes an anti-pollution mechanism combined with peer reputation and object reputation. Different from former research, we calculate the reputation of sharing files by the reputation of the voting peer. That is, object reputation is weighted by peer reputation. The reputation of the voting peer is built by its direct trust value and recommended trust value. Direct trust considers the trust of Direct Interaction Experience and the trust of Voting Similar Experience. Severe penalty strategy and voting incentive mechanism are introduced in calculating direct trust value. Therefore, a good user who uploads unpolluted files and voting on files actively can have a higher reputation, while a malicious user who uploads polluted files or voting unhonestly would have his reputation reduced sharply. Our expectation is that users for their own reputation will delect polluted files as soon as possible. These strategies give users an incentive to awareness of pollution, consequently isolate the polluters effectively. Our reputation mechanism is intended to prevent pollution spread by stimulating the awareness of users to delete files as soon as possible. At last, we simulate a P2P file sharing system to assess our reputation mechanism. The simulation results show that, compared to the object reputation system, our reputation mechanism convergence is faster, and has a better anti-pollution performance.

Research progress of trust evaluation model

Software systems in distributed environment are changing from a close and relatively static form, whose users are familiar with each other, to an open and highly dynamic mode, which can be visited by public. In such circumstance, trust evaluation model becomes focus of intense research at current time. Trust evaluation model establishes a management framework of trust relationship between entities, involving expression and measurement of trust, comprehensive calculation of direct trust value and recommended trust value, and recognition of malicious entities and recommendations. Based on the analysis of several typical trust evaluation models, the classification of trust evaluation ideas and modes is discussed, the questions existing in current research and the directions of future research are pointed out.