Mousmi Ajay Chaurasia

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. Plain Language Summary Unlike centralized health information systems, blockchain allows secure, decentralized storage of medical data, avoiding single points of failure. Furthermore, blockchain supports healthcare infrastructures with better accountability among hospitals, insurers, and patients. However, surveys and reviews reveal many proposed solutions. However, most privacy-sensitive healthcare blockchain systems are characterized by the following: · theoretical or tested only in limited-scale simulations, · little detailed performance optimization of cryptographic workloads and · storage requirements as well. However, blockchain in healthcare also brings drawbacks for privacy and efficient computing. This and more are discussed here. Key Takeaways - Off-chain actions are the latency bottleneck. - zk-SNARKs introduce privacy effectively. - Partially homomorphic encryption supports analytics without sacrificing security. - Hybrid key exchange substantially reduces cryptographic overhead.

A novel human-inspired metaheuristic framework, named Sastha Pilgrimage Optimization (SPO), mimics the optimization strategies inherent in the Sabarimala pilgrimage structure and the dynamics of pilgrims’ group behaviors. This methodology utilizes the significant characteristics of the practice, including Guruswamy’s selection (Leader), adaptive group interaction, and trade-offs between exploration and exploitation. It features a leader-based decision mechanism that balances individual performance with group harmony, guided by the most experienced participant. The exploration phase aims to cover a vast search space with dynamic updates in position. In contrast, in the exploitation phase, fine-tuning is achieved with adaptive coefficients and Lévy flights that guarantee convergence to global optima. The mathematical model of the algorithm incorporates new performance and distance threshold values, ensuring that route choices align with both individual and group objectives. A new human-inspired design by SPO can overcome the limitations of nonlinear, high-dimensional optimization challenges when compared to standard methods. The algorithm was benchmarked with over ten complex functions and validated on two datasets: the Cardiovascular dataset for feature selection and classification, and the Brain Tumor MRI dataset for image segmentation. These contributions make SPO a scalable, efficient, versatile optimization tool adaptable to domains requiring precise, high-dimensional decision-making.