Privacy-preserving deep learning approaches for effective utilization of wearable health data

Abstract

Wearable technology can significantly impact human existence by allowing for innovative perspectives on the outside world, such as through the use of augmented reality (AR) programs. Electronic gadgets that are worn as clothing, gadgets, or even incorporated into our bodies are called wearable gadgets. While there are many prospective advantages to smartwatches, their widespread and continuous use raises several privacy problems as well as complex data protection hurdles. Among the newer areas of healthcare informatics involves cognitive computation, which uses biological as well as physical information to autonomously track an individual's emotional status in mobility settings. While effective computation technologies hold great promise for enhancing everyday life, privacy problems and challenges must be addressed before analyzing physical information. Federated knowledge is a viable option for creating high-performing algorithms while protecting people's confidentiality. We used federated training to analyze cardiac activity information that was gathered from smart wristband tracking of anxiety levels during various situations. By protecting the confidentiality of information, we were able to accomplish promising outcomes when implementing federated training in Internet of Things-based wearable physiological tracking devices.

Keywords

• Privacy-preserving
• Deep learning
• Wearable health data
• Health informatics
• Data security
• Machine learning