A Distributed KBB Index Tree Multi-Keyword Fuzzy Sorting Search Scheme

SUN Jin; SONG Nana; WANG Lu; KANG Mengna; YE Kexin

doi:10.11999/JEIT250151

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SUN Jin, SONG Nana, WANG Lu, KANG Mengna, YE Kexin. A Distributed KBB Index Tree Multi-Keyword Fuzzy Sorting Search Scheme[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250151

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SUN Jin, SONG Nana, WANG Lu, KANG Mengna, YE Kexin. A Distributed KBB Index Tree Multi-Keyword Fuzzy Sorting Search Scheme[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250151

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A Distributed KBB Index Tree Multi-Keyword Fuzzy Sorting Search Scheme

doi: 10.11999/JEIT250151 cstr: 32379.14.JEIT250151

College of Science, Xi'an University of Science and Technology, Xi’an 710054, China

Funds: The Natural Science Foundation of Shaanxi Province of China (2021JM-341)

Received Date: 2025-03-11
Rev Recd Date: 2025-08-18

Available Online: 2025-08-27

Abstract

Abstract

Objective The rapid advancement of information technology has driven significant transformation in the medical domain. As a cornerstone of medical informatization, Electronic Health Records (EHRs) play a critical role in improving healthcare service efficiency and supporting medical research. Effective use of patient EHRs can enhance diagnostic and treatment processes and offer reliable data support for disease prevention and novel drug discovery. However, conventional EHR systems face several challenges, including data silos, secure sharing complexity, and heightened privacy risks. Data silos hinder the seamless exchange and integration of EHRs across institutions, limiting the potential for collaborative medical practices. A central challenge in secure data sharing lies in ensuring data interoperability without compromising patient privacy—a problem of urgent concern in modern healthcare systems. Moreover, privacy breaches not only jeopardize patient welfare but also damage the credibility and trustworthiness of healthcare institutions. To address these issues, this paper proposes a distributed multi-keyword fuzzy sorting search scheme based on a Keywords Balanced Binary (KBB) index tree. The scheme leverages the decentralized, tamper-proof, and traceable features of blockchain technology to create a secure and efficient framework for EHR sharing. By eliminating data silos, the blockchain facilitates cross-institutional interoperability, while smart contracts maintain strict privacy protections throughout the data sharing process. Methods The scheme first applies a K-means clustering algorithm to categorize EHRs, followed by hierarchical encrypted storage via the Interplanetary File System (IPFS). This dual-layer approach ensures distributed data management while protecting user privacy and enhancing storage robustness and fault tolerance. To improve search performance, the Porter stemming algorithm standardizes query keywords, reducing ambiguity from semantic variations and enabling consistent semantic matching. A KBB index tree is then constructed over the clustered EHRs to support fuzzy keyword matching and high retrieval precision. This structure incorporates adaptive sorting to reduce search latency. Access control is enforced using smart contracts, which implement fine-grained, role-based authentication to ensure that only authorized users can retrieve EHRs, thereby minimizing the risk of data leaks. During query processing, ranked searches are conducted within the KBB index tree. Once matching encrypted EHRs are located, data retrieval is performed via IPFS. Blockchain-based hashing ensures the integrity and immutability of the retrieved data, protecting it from tampering or corruption. Finally, users decrypt the data with private keys to access the original EHR content, completing the secure retrieval process. Results and Discussions Simulation experiments demonstrate that the proposed scheme offers superior implementation efficiency compared to related methods in terms of both time and storage overhead. The index tree construction time is shorter than that reported in[Ref. 7] and[Ref. 8], with an efficiency improvement of 80%–85% (Fig. 3). Although the trap generation time is longer than in[Ref. 7] and[Ref. 15] (Fig. 4), this increase stems from the scheme’s support for multiuser search, whereas[Ref. 7] only supports single-user search. The combined search time for indexing and trap generation is lower than in[Ref. 7],[Ref. 8], and[Ref. 15], with search efficiency improved by 50.5% and 75.8%, respectively (Fig. 5). The ciphertext generation and decryption times are also markedly better than those in the comparative literature, with improvements of 33.3% and 60%, respectively (Fig. 6, Fig. 7). Furthermore, the storage costs for keys and ciphertext in this scheme are lower than those of the other methods (Fig. 8, Fig. 9). Conclusions The proposed scheme substantially improves multi-keyword search efficiency, enabling users to rapidly locate target information within large-scale EHR datasets while enhancing the accuracy of search results. The fuzzy search functionality allows users to retrieve data even when the exact form of the keywords is unknown, increasing the flexibility and applicability of the search process. The encryption algorithm has been rigorously tested in experimental analyses, demonstrating that it maintains semantic security under the known ciphertext model, thereby ensuring EHR confidentiality. In addition, the algorithm performs well in terms of implementation efficiency, meeting the practical needs of EHR applications in medical contexts while maintaining strong security guarantees. By combining high-precision multi-keyword indexing with robust privacy protection, this scheme offers a scalable and secure framework for EHR sharing that meets the interoperability and confidentiality requirements of modern healthcare systems.
- Multi-keyword rank search,
- Keywords Balanced Binary (KBB) index tree,
- K-means clustering,
- Access control,
- Blockchain

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References(23)

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