EVQ: Enabling Verifiable Blockchain Keyword Query in Federated-Storage Edge Computing
Baochao Chen, Xiulong Liu, Hao Xu, Sheng Chen, Keqiu Li · 2025
Due to the exponential growth of blockchain ledger sizes, federated-storage which enables multiple devices to jointly store data, has emerged as a promising solution for secure data storage in edge computing. However, how to achieve verifiable queries in such decentralized storage remains underexplored. Existing broadcast-based query methods lack a verification mechanism for query results, making it impossible to ensure their correctness and completeness. Meanwhile, authenticated data structure based (ADS-based) query strategies are constrained by the full ledger data and cannot provide verifiable query services for users in a federated-storage environment. To this end, this paper takes the lead to propose EVQ, a verifiable blockchain keyword query scheme tailored for federated-storage edge computing. We propose a split keyword-based ADS as the core structure of our framework which ensures that users can verify the correctness and completeness of query results while alleviating storage pressure of edge devices. Specifically, the proposed ADS is constructed through a two-phase process: top-bottom keyword index tree construction and bottomtop RSA accumulator integration. Splitting the ADS based on keywords enables distributed data storage and the generation of corresponding ADS for the stored data. To reduce the query costs incurred by edge devices during query processing, we formulate the Keyword Allocation Optimization (KAO) problem and propose a gain-ratio-based keyword allocation mechanism to determine the splitting scheme of the ADS. The experiments are conducted based on the Foursquare dataset, which contains approximately 18 months of global check-in data collected from Foursquare. The experimental results show that, compared to the merkle tree strategy that also integrates the RSA accumulator, our EVQ improves query performance by 24.77 x.