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Medical data weights are predicted using XGBoost, and high-weight data are prioritized near blockchain root nodes to optimize storage architecture and shorten query paths. An efficient query method combines segmented filtering with aggregated Bloom filters and Merkle–Huffman (MH) trees to enhance on-chain performance. For reliable nonexistence proofs, multi-node collaborative verification integrates Bloom filters with a dynamic reputation mechanism for higher-credibility node selection and multi-node consensus, reducing false positives. Results show about a 15% query-efficiency improvement and improved security for resource-constrained mobile healthcare scenarios.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-novel-lightweight-medical-blockchain-data-query-scheme/450203/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-novel-lightweight-medical-blockchain-data-query-scheme/450203.png","ImageObject",300,407,{"name":92,"@type":93},"Oliver","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-07","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the proposed scheme address in lightweight medical blockchains?","Question",{"text":112,"@type":113},"It targets limitations in data query efficiency and the lack of reliable nonexistence proofs for medical data queries.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the scheme improve query efficiency?",{"text":117,"@type":113},"It uses XGBoost to predict medical data weights for storage prioritization and designs a query method combining aggregated Bloom filters with Merkle–Huffman trees to reduce query path length.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the scheme provide nonexistence proofs and prevent false positives?",{"text":121,"@type":113},"It introduces multi-node collaborative verification that integrates Bloom filters with a dynamic reputation system, adaptively selecting high-credibility nodes and using multi-node consensus to minimize false positives.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},450203,1791063774,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nA novel lightweight medical blockchain data query scheme  \nYunzhen Zhu1􀀍, Xiaohong Deng2,3􀀍, Jiayan Liu1, Yijie Zou2, Juan Li2 & Yuxin Fang2  \nTo address the limitations of lightweight medical blockchains in terms of data query efficiency and nonexistence proofs, we propose a novel lightweight medical blockchain data query scheme. First, the XGBoost algorithm is employed to predict medical data weights, with high-weight data storage near the blockchain’s root nodes being prioritized, thereby optimizing the storage architecture and enhancing query efficiency. Second, an efficient query method that combines aggregated Bloom filtersand Merkle–Huffman (MH) trees is designed. Through segmented filtering and weight optimization, the query path length is reduced, improving the on-chain data query performance. Finally, to address the challenge of the data nonexistence proof, we propose a multi-node collaborative verification mechanism that integrates Bloom filters with a dynamic reputation system. By adaptively selecting high-credibility nodes and employing multi-node consensus, false positives are minimized, ensuring query accuracy and reliability. Theoretical analysis and simulation results show that, compared with existing schemes, the proposed approach improves the query efficiency by approximately 15% . Moreover, integrating multi-node collaborative verification with a dynamic reputation mechanism effectively mitigates malicious attack risk and enhances system security, making it particularly suitable for resource-constrained scenarios such as mobile health care.  \nKeywords Medical blockchain, Data query, Bloom filter, Merkle tree, Huffman tree  \nMedical data sharing is crucial for resource integration and for optimizing diagnosis and treatment, ultimately improving patient experience and health care quality1. As the core component of medical data sharing, data queries directly impact the efficiency and accuracy of the shared system. However, current medical data query systems suffer from poor interoperability2, low efficiency3, and privacy leaks4. With its decentralized architecture, distributed storage, and tamper-resistant properties, blockchain technology provides a novel solution that enhances both the performance5 and security6 of medical data queries.  \nMost existing blockchain-based data query solutions are integrated with cloud storage models, where data are encrypted and stored in the cloud, while only index information is maintained in the blockchain. For example, Samala et al.7 proposed an electronic health record management system that enables data sharing and querying by storing data in the cloud and preserving index information in the blockchain. Kumari et al.8 introduced the HealthRec-Chain framework, which integrates the Interplanetary File System (IPFS) with blockchain technology to increase data security and query efficiency. Li et al.9 developed a blockchain-based distributed cloud storage system incorporating reliable deduplication mechanisms and storage balancing capabilities, which achieves sharded data distribution and load balancing through secret sharing and heuristic matching algorithms. However, while these advancements demonstrate notable progress in enhancing access efficiency for blockchain-based storage systems, the cloud storage implementation paradigm exhibits inherent limitations in system autonomy, degree of decentralization, and security assurance due to its reliance on third-party cloud resources.  \nTo achieve greater decentralization and data autonomy, data can be directly stored in the blockchain. However, as data continuously accumulate in the form of blocks, synchronizing the entire blockchain at each node significantly increases the storage and bandwidth demands. To address this issue, researchers have proposed lightweight blockchain architectures10, where resource-rich nodes act as full nodes, maintaining a ","cbCaihkXAzd691fr","https://ap.wps.com/l/cbCaihkXAzd691fr","pdf",6684399,22,"English","# Introduction\n## Motivation and challenges in medical data queries\n## Existing blockchain-based query approaches\n## Lightweight blockchain architectures\n## Related work and remaining gaps","[{\"question\":\"What problem does the proposed scheme address in lightweight medical blockchains?\",\"answer\":\"It targets limitations in data query efficiency and the lack of reliable nonexistence proofs for medical data queries.\"},{\"question\":\"How does the scheme improve query efficiency?\",\"answer\":\"It uses XGBoost to predict medical data weights for storage prioritization and designs a query method combining aggregated Bloom filters with Merkle–Huffman trees to reduce query path length.\"},{\"question\":\"How does the scheme provide nonexistence proofs and prevent false positives?\",\"answer\":\"It introduces multi-node collaborative verification that integrates Bloom filters with a dynamic reputation system, adaptively selecting high-credibility nodes and using multi-node consensus to minimize false positives.\"}]","A novel lightweight medical blockchain data query scheme | PDF",1790732439,55]