Peer-to-peer applications (P2P) are no longer limited to home users, and start being accepted in academic and corporate environments.While file sharing and instant messaging applications are the most traditional examples, they are no longer the only ones benefiting from the potential advantages of P2P networks. For example, network file storage, data transmission, distributed computing, and collaboration systems have also taken advantage of such networks. In this paper, we will present a summary of the main safety aspects to be considered in P2P networks, highlighting its importance for the development of P2P applications and systems on the Internet and deployment of enterprise applications with more critical needs in terms of security.....

INTRODUCTION: Due to the increasing trend of natural and manmade disasters in the contemporary world, especially in Iran, as well as the variety and high number of disasters in recent years, the issue of managing the humanitarian services supply network has become very important. The purpose of this study is to design and evaluate the model of trust building in the humanitarian services supply network, considering the importance of building trust in these networks and theoretical poverty in this field. METHODS: For doing this study, three recent major disasters in Tehran province, Iran, (Plasco accident, Shahran explosion and metro flood) were selected and had been studied and the trust building model in humanitarian service supply network is designed using Corbin and Strauss grounded theory version 2015 based on interviews with 8 humanitarian experts in that three disasters. Then, the relationships between the model’ s components were investigated by Structural Equation Modeling with a researcher-made questionnaire distributed among 128 individuals participating in the above events. FINDINGS: The final model is designed in three phases of conditions, actions, and consequences based on grounded theory. Poor community structures and community management systems are a major obstacle to trust building and political power interventions, insufficient funding, uncertainty of the responsible person, unsuccessful performance, lack of coordination and cooperation of the responsible working groups, and lack of readiness are the main factors of distrust. The main components of the model are highly correlated and the components have significant relationships. CONCLUSION: The research final model can solve the existing problems in Iran's humanitarian services supply network by building trust among organizations, members of the network and finally among the community people. Building trust enhances intra-network collaboration, and achieves the success and goals of the relief network through establishing greater coordination and cohesion. The dominance of the political factors in the relief network is also a major obstacle to obtaining the needed information and making good cooperation by those present in the network and thus, the involvement of political actors in the humanitarian supply network should be prevented.

Credit decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. Credit scoring models are decision support systems that take a set of predictor variables as input and provide a score as output and creditors use these models to justify who will get credit and who will not. Many different credit scoring models have been developed by the banks and researchers in order to solve the classification problems (i.e. distinguishing the good credit customers from the bad ones). Almost all these methods categorize the customers into two groups: the Good Credits and the Bad Credits. But regarding to the rapid growth in the number of credit applicants and also the intense competition between financial institutions, developing the models which are able to classify credit applicants into more groups (e.g. 6 or more), seems to be necessary. The purpose of this study is to propose an ANN- based algorithm which is capable of classifying the customers into 6 levels, regarding to their trust values. Till now, almost all of the studies in credit scoring are trying to improve the accuracy rate of the proposed algorithms and this is the first time that trust's concept is used in credit scoring domain. On the other hand, categorizing customers into more groups, will lead to make fast, easy, certain and fair credit lending decisions.

Because of the possibility of anonymity and impersonation in social networks, trust plays an important role in these networks. In social networks, trust can have two aspects: trust of users to social network and trust of users to other users. Pear to pear networks, by eliminating the supervisor roles, besides its benefit in decreasing management costs, have problems in trust and security of users. In these networks, trust evaluation is only related to the trust of pear to other pear and because of the direct relation between pears; each user should know the trust level of other users. However, trust evaluation in pear-to-pear networks (as an unsupervised network), only can be done based on the past relation between pears or trust evaluation of other pears. This kind of trust evaluation cannot give a comprehensive view to pears. In other word, if any pear is not in the friend cycle of a user or friend cycle of user's friends, he will not be able to assign appropriate trust level to this pear. In this research, by using social networks as supervised networks, trust level of each user is evaluated, then by identifying these users in unsupervised networks, appropriate trust level is assigned to them.

Peer-to-Peer systems have been the center of attention in recent years due to their advantage. Since each node in such networks can act both as a service provider and as a client, they are subject to different attacks. Therefore it is vital to manage confidence for these vulnerable environments in order to eliminate unsafe peers. This paper investigates the use of genetic programing for achieving trust of a peer without central monitoring. A model of confidence management is proposed here in which every peer ranks other peers according to calculated local confidence based on recommendations and previous interactions. The results show that this model identifies malicious nodes without the use of a central supervisor or overall confidence value and thus the system functions.