Privacy preservation using game theory in e-health application

In new e-health applications, the ubiquitous nature of intelligent devices raises legitimate questions about peoples’ privacy, and how to cope with the heterogeneity of user and application requirements in terms of security services. This requires the development of adaptive, context-aware and user-centric security solutions. Recent e-health applications (M2M: Machine-to-Machine/IoT: Internet of Things/Web) permit remote monitoring of patient health, medical treatments, fitness information and parameters, alarm triggering, etc. As monitored devices are tightly related to humans, they became able to act on their behalf, and may be modeled as attackers or defenders to make their decision autonomously. In this work, we propose a Markov based Theory Game Model (MTGM) between a data holder and a data requester in e-health applications to protect data privacy. The proposed solution ensures the highest payoff through the privacy-preserving decision taken to reach a compromise between privacy concession and incentive motivation. Numerical results of executed experiments are shown to confirm that game theory can enhance privacy protection and improve performance regarding time efficiency and information loss.