A Risk-Conscious Cybersecurity for Healthcare via Zebra-Inspired Optimization of Machine Learning Models*

Abstract

Healthcare organizations must start employing machine learning applications in their workplaces, regardless of fears surrounding patient privacy and regulations, due to their social and legal responsibilities to clients. This study presents the Zebra-Inspired Privacy Optimization (ZIPO), which emphasizes privacy and addresses the cybersecurity risks that hinder multiparty collaborative health data access. ZIPO demonstrates how bio-inspired algorithms can work with differential privacy principles and surpass federated learning algorithms, which already offer adaptive privacy calibration and enable patient data sharing across multi-institutional contexts while retaining anonymity. Through case-experimental study modeling with synthetic critical care unit records and simulated medical imaging data, ZIPO outperformed previous approaches, achieving 93.7% classification accuracy and guaranteeing differential privacy with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon&lt;0.9$</tex>. ZIPO also scored a 97.2% adherence across eight healthcare standards regulatory frameworks, such as ADA, HIPAA, and GDPR, which is a 12.8% improvement over previous privacy standards. Adaptive privacy parameter leverage was measured on the dataset for data sensitivity. To summarize, ZIPO has a cognitive automated gate-checking ability to simulate healthcare regulation compliance processes and produce quality documentation for implementation purposes in healthcare networks.

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