A social network is a social structure formed by the participants and their relationships. There are many studies and research papers regarding the various social networks. We regard a peer-to-peer supply chain network as a social network. On social networks, nodes' (participants') metrics regarding reputation and reliability are helpful information for forming and improving their relationships. Many researchers have proposed various mathematical models and node metrics on social networks. A network modeled as an edge-weighted directed graph is called a weighted signed network (WSN). We assume each node subjectively evaluates other nodes related to itself by scores and therefore refer to them as subjective scores. We obtain a weighted signed network by relating subjective scores to edge weights. There are many studies of methods to calculate the reputation and reliability metrics of nodes from the viewpoint of a whole network by using these subjective scores. However, subjective scores of each node tend to be confidential information for a person and organization. Nodes therefore wish to keep their scores confidential. This paper proposes exponentially convergent scores called 2-fairness and 2-goodness, for nodes of weighted signed networks and proposes a secure rating computation for them that keeps each node's subjective scores and related information secret.