  Reputation aggregation in peer to peer networks is generally a very time and
resource consuming process. Moreover, most of the methods consider that a node
will have same reputation with all the nodes in the network, which is not true.
This paper proposes a reputation aggregation algorithm that uses a variant of
gossip algorithm called differential gossip. In this paper, estimate of
reputation is considered to be having two parts, one common component which is
same with every node, and the other one is information received from immediate
neighbours based on the neighbours' direct interaction with the node. The
differential gossip is fast and requires less amount of resources. This
mechanism allows computation of independent reputation value by a node, of
every other node in the network, for each node. The differential gossip trust
has been investigated for a power law network formed using preferential
attachment \emph{(PA)} Model. The reputation computed using differential gossip
trust shows good amount of immunity to the collusion. We have verified the
performance of the algorithm on the power law networks of different sizes
ranging from 100 nodes to 50,000 nodes.
