BPI: A Novel Efficient and Reliable Search Structure for Hybrid Storage Blockchain
Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SP) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness, as the integrity of query results depends on the SPs' trustworthiness. Although ADS can verify the integrity of individual data points, they fall short of preventing SPs from omitting valid results. In this paper, we delineate the fundamental distinctions between data retrieval in blockchains and traditional database systems. Drawing upon these insights, we introduce the BPI framework, which employs a suite of validation models that ascertain the inclusion of all valid content in retrieval outcomes, with low overhead. We further present ''Articulated Search'', a query pattern specifically tailored for blockchain environments, which not only enhances retrieval efficiency but also substantially reduces costs during data user updates. Extensive experimental evaluations demonstrate that the BPI framework achieves outstanding scalability and performance in keyword searches within blockchain environments, surpassing EthMB+ and state-of-the-art search databases commonly used in mainstream hybrid storage blockchains (HSB). Notably, the Articulated Search pattern improves query performance by over three orders of magnitude, highlighting its potential as a transformative approach to blockchain query optimization.
Authors
- Gongsheng Yuan (ORCID: https://orcid.org/0000-0002-7992-3179)
- Xiaoye Miao (ORCID: https://orcid.org/0000-0002-8632-1539)
- Xiubo Liang (ORCID: https://orcid.org/0000-0002-4749-5552)
- Shuiguang Deng (ORCID: https://orcid.org/0000-0001-5015-6095)
- Jianwei Yin (ORCID: https://orcid.org/0000-0003-4703-7348)
- Xinhao Jin (ORCID: https://orcid.org/0009-0004-4337-6603)
- Guanjie Cheng (ORCID: https://orcid.org/0000-0003-2080-3903)
- Shawn Shi (ORCID: https://orcid.org/0009-0001-4282-8549)
- Xinkui Zhao (ORCID: https://orcid.org/0000-0002-1115-5652)
- R. Q. Xiong (ORCID: https://orcid.org/0009-0000-8083-7086)
Institutions
- Ningbo University (CN)
- Zhejiang University of Science and Technology (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Proceedings of the ACM on Management of Data
- Published
- 2025-12-04
- DOI
- https://doi.org/10.1145/3769766
- Primary Topic
- Data Quality and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00