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Reputation Aggregation in Peer-to-Peer Network Using Differential Gossip Algorithm.

ABSTRACT

In our proposed system estimate of reputation is considered to be having two parts, one common component which is same with every node, and the other one is the information received from immediate neighbours based on the neighbours’ direct interaction with the node. The differential gossip is fast and requires lesser amount of resources. This mechanism allows computation of independent reputation value by every node, of every other node in the network. The differential gossip trust has been investigated for a power law network formed using preferential attachment (PA) Model. The reputation computed using differential gossip trust shows good amount of immunity to the collusion.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:-     Existing  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. Tendency of nodes to draw resources from the network and not giving anything in return is termed as Free Riding. Aggregation of trust generally consumes a lot of time and memory especially with large number of nodes	PROPOSED CONCEPT: - In our proposed system we have been proposed we have proposed an algorithm that discourages the free riding and reduces the collusion significantly in networks with power law degree distribution. The proposed algorithm does not require any central server or repository.  The communication and computation cost for trust aggregation is low, and the aggregation is normally completed in reasonable time.
EXISTING TECHNIQUE:- Fuzzy-trust	PROPOSED TECHNIQUE:- Differential Gossip Algorithm
TECHNIQUE DEFINITION:- In Fuzzy-Trust , each peer maintains a local trust value and the transaction history with the remote peers. These weights are assigned on the basis of global trust value of the opining peer, transaction amounts and dates of transactions with the peers being evaluated.	TECHNIQUE DEFINITION:- Gossip Algorithms are used for spreading information in the large decentralised networks. These algorithms are random in nature as in these, nodes randomly choose their communication partner in each information diffusion step.
DRAWBACKS:- Generally consumes a lot of time and memory especially with large number of nodes. When reputation management systems are deployed,rogue peers will start using collusion to bypass it.	ADVANTAGES:- Generally simple, lightweight and robust for errors.   Discourages the free riding and reduces the collusion significantly in networks with power law degree distribution