Strategic frameworks: A review of game theory methods for privacy preservation in digital health

Abstract

With the advancement of technology and the transition towards a digital world, the field of health and medicine is rapidly evolving in this direction. To fully harness the benefits of digital health, it is crucial to address the associated challenges. Given the necessity of exchanging personal information between patients and healthcare centers over communication networks, ensuring security and preserving privacy are important challenging issues in this field. Various approaches have been proposed in the literature to tackle these challenges. Some studies have utilized game theory to analyze and model the issues of security and privacy. Over recent decades, game theory has proven its versatility in modeling and solving a variety of problems. Research indicates that game theory can significantly enhance healthcare outcomes, having been utilized across numerous specialties such as disease diagnosis, public health, cancer treatment, medical consultations, clinical decision-making, privacy, and security of medical information. In this paper, for the first time, we review game-theory-based methods for preserving privacy and security in digital health, categorizing them based on the types of games modeled. Our study results show that the most commonly used game models in this field are, in order, the Stackelberg, the Strategic, and the Evolutionary games. Based on the research conducted in each category of games, we extract the common model used so that these models can be utilized in future research.