Data Integrity and Causation Analysis for Wearable Devices in 5G

Abstract

The dissemination of information integrity at unprecedented speed and scale is a new phenomenon with the potential for vast harm if used incorrectly, specially applied in healthcare and clinical data. Despite holding much promise, the usefulness for clinical research using data from wearable devices that record user’s health conditions is limited by its integrity pitfall. This study presents and demonstrates a detection framework to effectively identify integrity compromises of wearable data and map the compromises with user scenarios under environmental influence. Through the Bayesian Network Model (BNM), the framework performs causation analyses between use scenario and data impact and integrates auto-encoder based data impact anomaly detection and classification. The auto-encoder based data impact detection eliminate the requirement for pre-training data, and enables a real-time detection with average latency of 4.6s. The BNM based causal inference shows accurate inference of user scenario based on the data impact detection. The proposed framework will allow for back tracing the root causes of the integrity compromises and trigger real-time human intervention to improve system integrity. We demonstrated system performance through a simulated use case.