Volume 4 Issue 2 International Journal of IEEE www.ijieee.com e-ISSN: 2321-0621 p-ISSN: 2321-063X

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Peer-to-Peer Network to Leverage the Aggregation Using the Differential Algorithm Gossip

S.MADHAVI1*,NARASIMHARAOSIRIVELLA2*1M.Tech–Student,DeptofCSE,2Asst.Prof,DeptofI.T.

QISCOLLEGEOFENGINEERING&TECHNOLOGY,VENGAMUKKALAPALEM,ONGOLE.

ABSTRACT:A peer-to-peer system, only one node on the basis of its own interaction with other colleagues should be expected to glory, but Based on the expression of the other nodes. To achieve the implementation of this strategy to leverage the aggregation policy. Reputation In the peer to peer networks are generally very time and resource consuming process of aggregation. Moreover, a lot of techniques A node in the network, which is not true, with the aggregation of all the nodes will then be in the same glory. This paper It uses a type of gossip is gossip differential algorithm is proposed that the name of the aggregation algorithm. In this paper, the estimated It is a normal part of the two parts of the glory that is the same for each node, and the other one The neighbor node information "received from the immediate neighbors on the basis of direct interaction. The differential is gossip Fast and requires the least amount of resources. This mechanism allows each node to calculate the value of an independent reputation Every other node in the network. Gossip is formed using the differential importance of the trust have been researching the power of the Law Network Attachment (PA) model. Gossip trust immunity to the differential is calculated using the good reputation of the size of the conspiracy. We are 50,000 nodes, with sizes ranging from 100 nodes networks, power law, which examined the performance of the algorithm.

Keywords: Trust, reputation, differential gossip, free riding, collusion

1.INTRODUCTION

PEER-TO-PEER systems have attracted considerable attention in recent past as they are more scalable than the client- server systems. In peer-to-peer system, there is no server. Each node acts as a server as well as a client. Free riding has emerged as a big challenge for peer-to-peer systems [1], [2]. Tendency of nodes to draw resources from the network and not giving anything in return is termed as Free Riding. Usually the nodes will have conflict of interests, thus the selfish behavior of nodes leads to the problem of free riding [3]. The behavior of nodes can be explained by famous prisoners’ dilemma [4]. In a file sharing network, if nodes are considered as players, their Nash Equilibrium(NE) will be the strategy where none of them are willing to share the resources [5]. Experimental studies [6], [7] on Gnutella network have confirmed this. In order to overcome the problem of free riding, peer-to peer networks can use trust or reputation management systems. As a result, one can also have the added advantage of resilience against certain kind of attacks [8], [9]. Such reputation management systems have been in use in the e-commerce portals like e-bay [10]; they are based on client server architecture and are much easier to implement and manage. When reputation management systems are deployed, rogue peers will start using collusion to bypass it.

2. RELATED WORKS

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Several techniques have been proposed in literature [18],[19] [20], [21], [22], [23] the glory for aggregation. Eigen- Trust [18], which means that much depends on the trusted peers That trusted peers around the world. This is a limited standard Extent. Peer-trust [19] Trust in the data (ie, the values stored in Trust All peers in the network in a distributed manner) of. The At each node is performed using a trust manager. In Peer-Trust, a peer node ID is used to determine the value of hash The value of the node is stored in the trust. In Fuzzy-Trust (Song et al. [20]), each peer maintains a local trust Value and transaction history with remote peers. Fuzzy inference is used to compute the aggregation Weights. These weights are assigned on the basis of the World opining Pier Trust, the value of the transaction amounts and Peers are appointed dates of the transactions. At During the collection, inquired about the value of the trust of the system Eligibility is blended from peers and peer evaluation To calculate the values received by the aggregation weights Trust values are updated. Trust calculated values In this way, there are global in nature. Power Trust [21] is another Trust is expected to make the difference. Each node keeps It is believed to have recorded scores of trusted nodes According to the quality of service received from them, interacted. This idea came from the energy distribution law degree track customer feedback on eBay transaction can be observed [21].Calculated by the aggregation of the world's most high Scores of local trust. And concluded that the node is known in the world of old sage Dim-weight as well as the trust. The value of trust Here are the world and all the peers in the network to use the The value of all transactions. Gossip algorithm Trust also found its use for aggregation. Gossip Trust

[22] networks to push for the gossip aggregation algorithm [24] uses the On the basis of a fully connected graph. This ensures The reputation of low overhead message aggregation. This technique is the aggregation of opinions Different nodes. Bloom filter structure Efficient use of the system for ranking. Kampe et al.[24] analyzed the data aggregation algorithm for the Gossip And came up with one in a complete graph-based network Top of the line convergence. He studied art at the expansion of the network. Research [18], [19], [21], [22] reported a lot, Each node has the same feel glory With everyone. Of course, it did not happen Due to interact differently with different nodes nodes Their selfish nature. When a node receives more Feedback from its neighbors, based on the different Depending on the reputation of the neighborhood. In the In this paper, we estimate the local reputation are different, Trust and comprehensive, as reported in the neighborhood The common glory. Is the aggregation The proposed algorithm is done by the diversity of gossip Power Law rapid convergence in the network. We are Given an algorithm that combines the above activities. It is expected to be discouraged by colluding nodes The proposed algorithm.

1.AGGREGATION OF TRUST

When a node needs a resource, it will ask from the neighborhood; If they have a resource, the node gets the answer of Its query. You do not have neighbors, they forward Interviewed their neighbors and so on. Node Resource, replies back to the requesting node. Requesting Now asks for resource from the node to node. The According to the source node provides the answer

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directly Reputation as requested. A node receives a request from a non-neighbor The reputation of the requesting node to another node In order to determine the assessment of the quality of something Service provided. However, two nodes are going to make transactions They have the reputation to be the first time Other. This can be done by getting a reputation And then from the neighboring node is already using it An initial estimate. For a node, multi-trust values Therefore, we need to get collectively approach The value of the Trust. The value of the trust should lie in ever Between zero and one, with zero meaning no trust and a means Full Trust.

3.CONCLUSION

Peer-to-peer networks, free riding is a major problem Can be overcome by using reputation management System. Both systems also have a reputation management system,The fame and the first estimate of the second aggregation Reputation. In this paper we propose a collective, By modifying the technique for a power law networks Gossip Gossip algorithm, the algorithm of differential push, push.The proposed aggregation technique, efficiently aggregates Trust values from different nodes of a power law

Network. This technique does not require recognition Electric nodes. This algorithm makes it easy to deploy There is recognition of the power of the nodes in a distributed setting Hard. This algorithm is robust against slipping as much as possible Can be seen in Fig. 4. The suggested technique aggregates glory A differential manner. This is done by considering Feedback trusted nodes with higher weight. The As is evident from Fig leads to robustness against collusion. 5.The proposed algorithm has been presented to However, it can also be used to avoid harmful free riding problem By changing the method of network users AI and assessment bij.

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[5] Y. Tang, H. Wang, and W. Dou, “Trust based incentive in p2p network,” in Proc. IEEE Int. Conf. E-Commerce Technol. Dynamic EBus., Washington, DC, USA, 2004, pp. 302–305. [6] E. Adar and B. A. Huberman, “Free riding on gnutella,” First Monday, vol. 5, p. 2000, 2000. [7] D. Hughes, G. Coulson, and J. Walkerdine, “Free riding on gnutella revisited: The bell tolls?” IEEE Distrib. Syst. Online, vol. 6,no. 6, p. 1, Jun. 2005. [8] K. Chen, K. Hwang, and G. Chen, “Heuristic discovery of rolebased trust chains in peer-to-peer networks,” IEEE Trans. Parallel Distrib. Syst., vol. 20, no. 1, pp. 83–96, Jan. 2009. [9] E. Damiani, S. De Capitani Di Vimercati, S. Paraboschi, and P. Samarati, “Managing and sharing servants’ reputations in p2p systems,” IEEE Trans. Knowl. Data Eng., vol. 15, no. 4, pp. 840–854, Jul.-Aug. 2003. [10] [Online]. Available: http://www.ebay.com [11] S. Saroiu, P.Gummadi, and S. Gribble. (2002).Ameasurement study of peer-to-peer file sharing systems [Online]. Available: http:// citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.4223 [12] D. Group, “Gnutella: To the bandwidth barrier and beyond,” 2000, http://lambda.cs.yale.edu/cs425/doc/gnutella.html [13] H. Chen, H. Jin, J. Sun, D. Deng, and X. Liao, “Analysis of largescale topological properties for peer-to-peer networks,” in Proc. IEEE Int. Symp. Cluster Comput. Grid, 2004, pp. 27–34.

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AUTHOR’S PROFILE:

Sudhanagunta Madhavi,

M.Tech Computer Science and

Engineering from QIS College Of

Engineering &Technology.

Sirivella Narasimha Rao,

Working as Assistant professor in

Department of Information

Technology from QIS College Of

Engineering &Technology.