Despite the ubiquitous nature of wearables in today’s society, there are increasing concerns with respect to the security and privacy of personal data stored in these devices. Private data and health information stored in wearables require a device and a third party to transmit user information. An authentication method is then required to verify the transmission. This method can be done in many different ways, but advancements in biometric authentication models have led to the exploration of utilizing Electrocardiogram (ECG) signals as a model for developing encryption and decryption methods for private and sensitive information from wearable devices.
Researchers at Arizona State University have developed a system for encrypting and decrypting private data on wearable devices using ECG signals. A user’s ECG signals are used as a unique source for making Physical Unclonable Function (PUF) keys for encryption and decryption engines. A signal processing unit embedded in the system detects and filters ECG signals of interest transmitted by the user. Machine learning algorithms then learn the user’s personal ECG signals to generate a unique 256-bit PUF key for encrypting and decrypting information on wearable devices.
- Large Scale Security
- Private and Secure Health Monitoring
- Extra Security Layer for Biometric Authentication
Benefits and Advantages
- Active – Access to information is only allowed when worn by the owner
- Tough – Keys are extremely difficult to reproduce, even by manufacturers
- Intelligent – System algorithms learn the user’s unique ECG signals as they change over time
- Established – Tests on the system pass the National Institute of Standards and Technology (NIST) randomness tests
For more information about the inventor(s) and their research, please see