Authenticated Aggregate Queries with Boolean Range Predicates on Blockchains

Blockchains have gained wide adoption for secure data processing. As blockchain data volumes grow, the demand for efficient data analysis, especially aggregate queries, becomes increasingly critical. However, current blockchains lack native support for efficient analytical query processing, forcing users to either maintain full replicas or rely on third-party services without integrity guarantees. In this paper, we propose an efficient framework, Merkle Bloom Filter Tree (MBFT), for authenticated aggregate queries that combine boolean keywords and range predicates on blockchains. At its core is a Bloom filter-based authenticated data structure that supports both types of predicates, constructed per block for efficient transaction indexing. For temporal predicates, we optimize time window queries through value pruning and block consolidation. We design a novel Merge Bloom Filter (MBF) for space-efficient handling of dynamic sets during query authentication. We provide a theoretical analysis of the storage overhead caused by the Bloom filter’s false positive rates. Our framework employs data sketches to support various aggregate operations. Extensive experiments demonstrate that MBFT has improved the query speed by up to 286× compared to state-of-the-art authenticated query solutions.