Performance Optimization in Blockchain Networks for Healthcare Systems Using Adaptive Sharding

Abstract

Blockchain technology has the potential to transform healthcare data management by enhancing security, transparency, and data integrity. However, scalability, latency, and privacy concerns have limited its application in high-volume, sensitive environments such as healthcare. This paper introduces a blockchain architecture that addresses these challenges through adaptive sharding and rule-based data partitioning. The adaptive sharding algorithm dynamically adjusts shard configurations in response to real-time network demands, optimizing resource allocation and improving scalability. Meanwhile, rule-based data partitioning organizes transactions across shards based on specific attributes, such as transaction type or geographic region, to minimize cross-shard communication and improving processing efficiency. Together, these methods increase transaction throughput by 34% and reduce latency by 9% compared to traditional approaches. Additionally, the system incorporates Byzantine Fault Tolerance (BFT) consensus to strengthen security, along with zero-knowledge proofs and homomorphic encryption to protect sensitive patient data during transaction verification. This architecture provides a comprehensive, scalable, and secure blockchain solution tailored to the unique needs of healthcare data management, addressing critical limitations while maintaining privacy and data integrity.