A Federated Neuro-Symbolic Deep Learning Framework with Zero-Knowledge Blockchain for Electronic Health Data Security

Abstract

The need for strong cybersecurity frameworks in the healthcare industry has increased due to the Internet of Medical Things (IoMT) devices and electronic health records (EHRs) exponential growth. This paper presents HealthSentinel-ZKP, a novel framework that leverages federated neuro-symbolic deep learning and zero-knowledge blockchain to secure electronic health data. In contrast to earlier models, HealthSentinel-ZKP combines CNNs, Transformers, and symbolic reasoning for multi-perspective intrusion detection. Federated learning and zero-knowledge proof mechanisms protect data privacy. Using an immutable ZKPenhanced blockchain, the system guarantees GDPR-compliant auditing and has a dual-stream anomaly detection architecture. HealthSentinel-ZKP is a next-generation healthcare cybersecurity paradigm, as demonstrated by experimental results on benchmark datasets that demonstrate superior performance in zero-day attack detection and privacy preservation.