  To mitigate the attacks by malicious peers and to motivate the peers to share
the resources in peer-to-peer networks, several reputation systems have been
proposed in the past. In most of them, the peers evaluate other peers based on
their past interactions and then aggregate this information in the whole
network. However such an aggregation process requires approximations in order
to converge at some global consensus. It may not be the true reflection of past
behavior of the peers. Moreover such type of aggregation gives only the
relative ranking of peers without any absolute evaluation of their past. This
is more significant when all the peers responding to a query, are malicious. In
such a situation, we can only know that who is better among them without
knowing their rank in the whole network. In this paper, we are proposing a new
algorithm which accounts for the past behavior of the peers and will estimate
the absolute value of the trust of peers. Consequently, we can suitably
identify them as a good peers or malicious peers. Our algorithm converges at
some global consensus much faster by choosing suitable parameters. Because of
its absolute nature it will equally load all the peers in network. It will also
reduce the inauthentic download in the network which was not possible in
existing algorithms.
