Information Flows for Athletes' Health and Performance Data
Brad Stenger, Yuanyuan Feng
TL;DR
The paper investigates information flows for athletes' health and performance data through the lens of contextual integrity, defining team-centric and athlete-centric patterns and examining the roles of teammates, injuries, vendors, and organizational hierarchies. It analyzes privacy risks inherent in current flows and highlights governance constraints created by sports hierarchies. It then introduces differential privacy (DP) and proposes two new flows, research-centric and community-centric, to enable larger-scale, privacy-preserving sharing via a transmission properties parameter in CI; DP is formalized by $(\
Abstract
Increasing numbers of athletes and sports teams use data collection technologies to improve athletic development and athlete health with the goal of improving competitive performance. Personal data privacy is managed but it is not always a priority for the coaches who are in charge of athletes. There is a pressing need to investigate what are appropriate information flows as described by contextual integrity for these data technologies and these use cases. We propose two main types of information flows for athletes' health and performance data -- team-centric and athlete-centric -- designed to characterize data used for the collective and individual physical, psychological and social development of athletes. We also present a scenario for applying differential privacy to athletes' data and propose two new information flows -- research-centric and community-centric -- which envision larger-scale, more collaborative sharing of athletes' data in the future.