Research on the Application of Spark in Medical Big Data Analysis under the Background of Blockchain

Medical big data holds significant value in promoting precision medicine, disease prediction, and public health management. However, issues such as sensitivity, decentralization, and privacy security limit its in-depth application. This study proposes a collaborative computing framework based on blockchain and Apache Spark, aiming to address the challenges of privacy protection, cross-institutional sharing, and efficient analysis of medical data. By designing an access control mechanism based on smart contracts and an anonymization scheme utilizing zero-knowledge proofs, combined with Spark's distributed memory computing advantages, a secure and trustworthy platform for medical data analysis is constructed. Experiments demonstrate that this framework improves data processing efficiency by 3.5 times compared to the traditional Hadoop architecture on the MIMIC-III dataset, while also meeting HIPAA privacy standards. This study provides theoretical support and practical pathways for the application of "blockchain + big data" technology in the medical field.