Lightweight Privacy-Preserving Blockchain Framework for Healthcare: A Simulation-Based Approach to Reducing Computational Overhead
TL;DR
This work demonstrates that efficient cryptographic integration and optimization through simulation can produce a privacy-preserving blockchain for healthcare that streamlines EHR handling securely and at scale.
Abstract
Background The privacy and security of electronic health records (HER) in blockchain-based systems remains a major research problem because of high computational overhead and scalability restrictions. Privacy-preserving techniques such as encryption and zero-knowledge proofs strengthen blockchain’s transparency and immutability but often add significant latency and resource use. This study proposes a lightweight, simulation-based blockchain model balancing privacy protection and computational efficiency for healthcare data-sharing, incorporating hybrid encryption (AES with asymmetric-key exchange), zero-knowledge verification (zk-SNARK), and homomorphic aggregation to protect patient information while reducing processing cost. Methods A five-stage simulation tested encryption/decryption latency, IPFS-based upload/download performance, proof generation/verification time, and scalability across key sizes, plus a sixth phase validating the framework on two real, publicly available, de-identified healthcare datasets—the Medical Information Mart for Intensive Care (MIMIC)-IV demo (100 real ICU patients) and the University of California “Diabetes 130-US Hospitals” dataset (101,766 real inpatient encounters). Every metric is reported as a mean with a 95% confidence interval from 15 to 20 repeated trials. Results The AES-128 has the lowest overhead among tested key sizes (10% to 14% below AES-192/256), zk-SNARK verification averages 30.8 to 32.4 ms (n = 20 to 100 trials)—well within real-time requirements for on-chain access decisions—and proof generation and gas cost are statistically indistinguishable between a minimal baseline circuit and the consent-verification circuit, indicating negligible marginal overhead from the added consent logic. Computing cost scales linearly with data size: confirming lightweight scalability, with real-data results closely tracking synthetic-data results, with a narrowly scoped comparison showing error correction code memory (ECC (memory (secp256r1) key exchange is 93.5% faster than RSA-3072 key wrapping. Conclusions This work demonstrates that efficient cryptographic integration and optimization through simulation can produce a privacy-preserving blockchain for healthcare that streamlines EHR handling securely and at scale.