Facial videos can reveal hidden health information, such as heart rate, by analyzing subtle changes in skin color. While this can help with remote health monitoring, it also poses serious privacy risks, as health data can be extracted without permission. In this study, we propose a method to protect such information. Our approach allows health signals to be removed from a video, encrypted, securely transmitted, and later restored by an authorized recipient. This reversible method keeps the video looking natural while safeguarding sensitive health data against misuse. We test our framework on a set of real videos and show that it works reliably across different activities. This work could improve privacy in video calls, telehealth, and other everyday video applications.

The revolutionary remote photoplethysmography (rPPG) technique has enabled intelligent devices to estimate physiological parameters with remarkable accuracy. However, the continuous and surreptitious recording of individuals by these devices and the collecting of sensitive health data without users’ knowledge or consent raise serious privacy concerns. Here we explore frugal methods for modifying facial videos to conceal physiological signals while maintaining image quality. Eleven lightweight modification methods, including blurring operations, additive noises, and time-averaging techniques, were evaluated using five different rPPG techniques across four activities: rest, talking, head rotation, and gym. These rPPG methods require minimal computational resources, enabling real-time implementation on low-compute devices. Our results indicate that the time-averaging sliding frame method achieved the greatest balance between preserving the information within the frame and inducing a heart rate error, with an average error of 22 beats per minute (bpm). Further, the facial region of interest was found to be the most effective and to offer the best trade-off between bpm errors and information loss.