A Security-Focused Architecture for Gameplay Telemetry in Serious Games

Serious games are a promising approach for diagnostic and therapeutic as well as educational purposes. Capturing player performance and behavior along with details of gameplay directly from the game is a valuable data source for evaluating serious games and for their iterative refinement. In many serious games, the collected data can however be especially sensitive for privacy, as it may imply learning or health state progression, and thus should be strongly protected. In this paper, we specify goals for a security-enhanced game telemetry system, model a security architecture based on them, and present the design of such a system, which we implemented for evaluation of health games in our research group. The presented system is designed to be applicable for various types of serious games. It employs end-to-end encryption, signatures, and data separation to improve protection of collected data without the addition of usability burdens on players from these security measures.