CubeChain: Generalized Query Framework for Intra- and Cross-Chain Scenarios

With the rapid expansion of blockchain data, the demand for data exchange between chains has grown significantly. Authenticated queries have become one of the crucial methods for retrieving on-chain data due to their efficient performance and ability to ensure data security. However, existing intra-chain query approaches either face substantial maintenance overhead or exhibit low query efficiency, when dealing with the explosive growth of data in cross-chain scenarios; while current cross-chain query approaches suffer from issues including limited query types and poor scalability. To this end, this paper takes the lead to propose a novel generalized framework named CubeChain which provides various query types for intra- and cross-chain authenticated queries. We propose a highly scalable authenticated data structure (ADS) named Cube as the core structure of our framework which excels in achieving high performance while minimizing maintenance overhead by establishing data bridges between vertexes. When implementing CubeChain, we address two challenges: (i) implementing lightweight verification while supporting various query types by using a two-layer hashing structure, and (ii) further improving the query efficiency by suppressing vertexes. We substantiate the superior performance of Cube in terms of query efficiency, update overhead, and scalability through theoretical analysis. Finally, we implement the CubeChain framework based on the open-source Fabric v2.2. Real experiments with YCSB benchmark demonstrate that, compared with the state-of-the-art Bs+tree-based ADSs in MSTDB and SEBDB, our query performance improved by 23.75x in intra-chain scenarios and 10.72x in cross-chain scenarios, while maintaining a 30% reduction of cross-chain query load.