Privacy-Preserving Outsourced Nash Equilibrium Computation in Cloud Computing

Abstract

AbstractIn a non-cooperative game, the Nash equilibrium is computed by the game players with the payoff matrix. With the obtained Nash equilibrium, game players can analyze equilibrium strategies and make optimum decisions. In cloud computing, the computation of Nash equilibrium may be outsourced to the cloud due to the constrained resources of players. However, the payoff matrix may be sensitive and needs to protect against the cloud server. In this paper, we propose a cloud-based framework to secure outsourcing the task of computing mixed-strategy Nash equilibria. In our framework, the payoff matrix is encrypted with additive homomorphic encryption before uploaded and stored in the cloud. By combining secure multi-party computing techniques, we enable the cloud server to compute Nash Equilibria on the encrypted data without disclosing sensitive users’ data. We conduct experiments to verify the effectiveness, evaluate the precision, and then analyze the computational complexities.KeywordsGame theoryNash equilibriaPrivacy preservingHomomorphic encryptionCloud computing