Continuous Behavioral Biometrics for Passwordless Authentication: A Trust Engine - Privacy Preserving, On Device Trust Engine for Mobile and Wearables

In current digital systems, passwords also constitute a significant source of weakness as they have been linked to more than eighty percent of data attacks throughout the world. Behavioral Biometrics, a type that examines behaviours like typing speed, single-hand motions, and footprints, provide continuous authentication, although most systems are centralized, presenting privacy concerns and lag. This paper introduces a privacy-sensitive on-device, trust engine of constantly authenticated behavior on mobile and wearable devices. The system converts real-time behavioral indicators, resulting in a dynamic trust score, allowing passwordless authentication and retaining all the raw data locally. Publication tests and practice logs in practice prove high accuracy with a True Acceptance Rate of over 90 percent, and with a False Acceptance Rate of under 5 percent and Equal Error Rate of about 6.5 percent. Latency is less than 50 milliseconds per assessment, and memory footprint is less than 50 MB. The solution identifies suspicious activity, promotes fallback with multiple factors, and is consistent with the principles of zero-trust. This framework would offer a feasible, secure, and privacy-aware metric of mobile, wearable, and IoT surroundings, sealing the insufficiencies of traditional biometrics as well as network-level trust frameworks.