Peer-to-Peer networks are designed to rely on resources of their own users. Therefore, resource management plays an important role in P2P protocols. Therefore, resource management plays an important role in P2P protocols. Early P2P networks did not use proper mechanisms to manage fairness. However, after seeing difficulties and rise of freeloaders in networks like Gnutella, the importance of providing fairness for users have become apparent. In this paper, we propose an incentive based security model which leads to a network infrastructure that lightens the work of Seeders and makes Leechers to contribute more. This method is able to prevent betrayals in Leecher-to-Leecher transactions and more importantly, helps Seeders to be treated more fairly. This is what other incentive methods such as Bittorrent are incapable of doing. Additionally, by getting help from cryptography and combining it with our method, it is also possible to achieve secure channels, immune to spying, next to a fair network. The simulation results clearly show that how our proposed approach can overcome free-riding issue. In addition, our findings revealed that our approach is able to provide an appropriate level of fairness for the users and can decrease the download time.
Trust management systems are methods to establish word-of-mouth for P2P networks. This means that based on transactions between nodes, they evaluate a degree of trust for each node which is mostly used for establishing a fair network. Finding a malicious user from its neighbors is a selection problem. Methods such as fuzzy decision making @cite_20 @cite_25 and genetic-based algorithms @cite_23 can be used to select the free-riders from multiple criteria. A successful trust management system that uses this approach is @cite_5 . The @cite_18 @cite_4 are other examples of trust management systems. EigenTrust is the name of an approach proposed In @cite_1 . This method uses an algorithm to decrease the number of downloads of inauthentic files in a P2P network that assigns each peer a unique global trust value, based on the peer's history of uploads. For this purpose, the authors proposed a distributed and secure method to compute global trust values, based on power iteration. These values are then used by peers to select the peers from whom they download.
@cite_18 @cite_4 @cite_1 @cite_23 @cite_5 @cite_25 @cite_20
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An important issue in Peer-to-Peer networks is to encourage users to share with others as they use the resources of the network. However, some nodes may only consume from users without giving anything in return. To fix this problem, we can incorporate trust management systems with network infrastructures. Current trust managements are usually made for unstructured overlays and have several shortcomings. They are made to be very similar to e-commerce scoring websites which may not be the best design for fairness in P2P networks. Several problems may arise with their designs such as difficulties to provide a complete history of freeloaders or lack of an autonomous removal mechanism in case of severe attacks. In this paper, we argue that such systems can be deployed more efficiently by using a structured paradigm. For this purpose, we propose C-Trust, a trust management system which is focused on fairness for P2P networks. This is done by getting help from current circular structured designs. This method is able to mark freeloaders, identify their severity of abusion and punish them accordingly. We are also able to effectively protect both Seeder-to-Leecher and Leecher-to-Leecher transactions. This feature is specially important for fairness which other trust systems have not considered so far. Managing trust is a problem of particular importance in peer-to-peer environments where one frequently encounters unknown agents. Existing methods for trust management, that are based on reputation, focus on the semantic properties of the trust model. They do not scale as they either rely on a central database or require to maintain global knowledge at each agent to provide data on earlier interactions. In this paper we present an approach that addresses the problem of reputation-based trust management at both the data management and the semantic level. We employ at both levels scalable data structures and algorithms that require no central control and allow to assess trust by computing an agents reputation from its former interactions with other agents. Thus the meethod can be implemented in a peer-to-peer environment and scales well for very large numbers of participants. We expect that scalable methods for trust management are an important factor, if fully decentralized peer-to-peer systems should become the platform for more serious applications than simple file exchange. Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread of self-replicating inauthentic files.We describe an algorithm to decrease the number of downloads of inauthentic files in a peer-to-peer file-sharing network that assigns each peer a unique global trust value, based on the peer's history of uploads. We present a distributed and secure method to compute global trust values, based on Power iteration. By having peers use these global trust values to choose the peers from whom they download, the network effectively identifies malicious peers and isolates them from the network.In simulations, this reputation system, called EigenTrust, has been shown to significantly decrease the number of inauthentic files on the network, even under a variety of conditions where malicious peers cooperate in an attempt to deliberately subvert the system. Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of time. As it is important to do business with trustful individuals and companies, there is a need to survive the reputation concept in virtual markets. Auction sites generally employ reputation systems based on feedbacks that provide a global view to a cyber dealer. In contrast to global trust, people usually infer their personal trust about someone whose reputation is completely or partially unknown by asking their trusted friends. Personal reputation is what makes a person trusted for some people and untrusted for others. There should be a facility for users in a virtual market to specify how much they trust a friend and also a mechanism that infers the trust of a user to another user who is not directly a friend of her. There are two main issues that should be addressed in trust inference. First, the trust modeling and aggregation problem needs to be challenged. Second, algorithms should be introduced to find and select the best paths among the existing trust paths from a source to a sink. First, as trust to a person can be stated more naturally using linguistic expressions, this work suggests employing linguistic terms for trust specification. To this end, corresponding fuzzy sets are defined for trust linguistic terms and a fuzzy trust aggregation method is also proposed. Comparing the fuzzy aggregation method to the existing aggregation methods shows superiority of fuzzy approach especially at aggregating contradictory information. Second, this paper proposes an incremental trust inference algorithm. The results show improvement in preciseness of inference for the proposed inference algorithm over the existing and recently proposed algorithm named TidalTrust.