Privacy-Preserving Digital Identity in Resilient Healthcare Ecosystems With AI, IoT, and Blockchain

Abstract

Digital identity in healthcare has evolved from a convenience into a necessity, yet its dependence on centralized authentication continues to expose systems to privacy breaches and operational fragility. Existing identity models, though secure in principle, often collapse under real-world conditions where IoT devices, patient data streams, and network failures coexist. Most frameworks optimize for privacy or performance but rarely both. This study proposes a Resilient Privacy-Preserving Digital Identity Framework (RePP-DIF) that fuses artificial intelligence (AI), Internet of Things (IoT), and blockchain to achieve adaptive and fault-tolerant authentication within healthcare networks. The framework integrates a CNN–LSTM edge predictor for anomaly detection, zero-knowledge proofs for selective credential disclosure, and a replica consensus mechanism to sustain verification during validator failures.

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