Cooperation of agents is imperative for information consensus in a network, but confidentiality issues might discourage certain agents from participating in the distributed consensus algorithms. This paper proposes a novel distributed average consensus algorithm which preserves the confidentiality of every cooperating agent's initial state value from other cooperating agents in the network, while asymptotically achieving the desired average of the initial state values of the agents. The proposed algorithm requires minimal change in the widely-known graph Laplacian based linear consensus algorithm and imposes minimal additional computational load on the participating agents.