Incorporating social interaction into three-party game towards privacy protection in IoT

Abstract

By exploiting rich personal information, Internet of Things can provide users with various customized experience and services, improving entertainment, convenience and quality for users’ life. However, unavoidably, these users suffer from serious risk of privacy leakage in the presence of untrusted service provider and malicious adversary. Game theory is treated as one of the most promising methodologies to investigate participants’ incentive, response, and behaviors and has been widely applied to design privacy preserving schemes. Nevertheless, the complex interactions among users, service provider, and adversary are not fully investigated in the existing work. What’s more, users’ social connection and interaction are ignored. In this paper, such complex interactions are modeled as a three-party game for the problem of private data trading in IoT with considering user’s social interaction in online social network. Particularly, data trading between service provider and adversary is formulated to be a Nash bargaining game, for which Nash bargaining solutions are analyzed via both theoretical analysis and numerical experiments. Our analysis can clearly illustrate data trading strategies between service provider and adversary and offer guidance for designing privacy protection scheme in IoT.