Dynamic Access Decision Scoring: An Adaptive Framework for Health care Data Security and Privacy
Abstract:
This paper introduces a novel Dynamic Access Decision Scoring (ADS) framework that 1 integrates cognitive com puting and big data to address emerging challenges in controlling access 2 to healthcare data systems. Traditional rule-based access control mechanisms lack the cognitive 3 capabilities to process dynamic security requirements, creating vulnerabilities when managing large-4 scale electronic health records (EHRs). Our framework leverages cognitive computing by combining 5 machine learning algorithms, behavioral pattern analysis, and real-time data analytics to create an 6 intelligent security system that safeguards sensitive medical data while maintaining compu tational 7 efficiency. The core innovation lies in developing a cognitive mathematical template that data 8 scientists and researchers can adapt through deep learning and analytical processing. The framework 9 introduces a modular formula as an adaptive cognitive pattern, incorporating four computational 10 elements: machine learning predictions, historical pattern recognition, risk analytics, and temporal 11 context processing. Each element employs cognitive algorithms that security architects can calibrate 12 within their specific data ecosystems. The framework’s primary contribution demonstrates how 13 cognitive probabilistic approaches can dynamically adapt to complex healthcare environments. 14 This research advances big data security by estab lishing a cognitive computing foundation for 15 making access control decisions, effectively bridging theoretical data models with practical machine 16 intelligence implementation in healthcare information systems.
