VTRQ：在混合存储区块链中实现可验证的轨迹范围查询
VTRQ: Enabling Verifiable Trajectory Range Queries in Hybrid-Storage Blockchains
中文总结 AI 辅助
VTRQ是首个混合存储区块链中可验证轨迹范围查询框架，含两种ADS及时空边聚合机制，解决现有混合存储区块链轨迹数据可验证查询支持不足的问题，提升了查询与验证效率。
中文摘要 AI 辅助
由于轨迹数据量日益庞大，将轨迹存储与查询外包给第三方服务提供商已颇具吸引力。然而，在这种外包环境中，服务提供商可能返回不正确的查询结果，例如不完整、被篡改或无效的结果，使得查询结果的可验证性成为重要考量。现有的混合存储区块链对轨迹数据的支持有限，缺乏能实现高效验证的认证数据结构（ADS）。例如，专为一维数据查询设计的ADS不适用于多维轨迹数据的查询，而专为离散数据定制的ADS应用于连续轨迹数据时可能产生不完整的结果。我们提出了首个用于混合存储区块链中可验证轨迹范围查询的框架，名为VTRQ。它包含两种高效的ADS：（i）针对道路网络的空间ADS，利用分层组织聚合轨迹、边和节点的哈希值，从而减少冗余计算并提高空间验证效率；（ii）基于区间树的时间ADS，仅索引轨迹的开始和结束时间，从而实现剪枝并提升时间验证效率。通过分离空间与时间索引，该方法减少了数据比较需求，提升了查询与验证效率。为聚合空间和时间查询结果，VTRQ提供了时空边聚合机制，结合空间节点的时间验证、空间交集计算和时间交集分析，实现时空过滤。
英文摘要
Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environments, service providers may return incorrect, e.g., incomplete, tampered, or invalid query results, making verifiability of query results an important consideration. Existing hybrid-storage blockchains offer limited support for trajectory data, lacking authenticated data structures (ADS) that enable efficient verification. For example, ADSs designed for queries on one-dimensional data are unsuitable for queries on multidimensional trajectory data, while ADSs tailored for discrete data may yield incomplete results when applied to continuous trajectory data. We propose the first framework for verifiable trajectory range queries in hybrid-storage blockchains, called VTRQ. It features two efficient ADSs: (i) a spatial ADS for road networks that leverages hierarchical organization to aggregate trajectory, edge, and node hashes, thus reducing redundant computations and improving spatial verification efficiency; and (ii) a temporal ADS based on interval trees, which indexes only the start and end times of trajectories, thereby enabling pruning and efficient temporal verification. By separating spatial and temporal indexing, the method reduces the need for data comparison, enhancing both query and verification efficiency. To aggregate spatial and temporal query results, VTRQ provides a spatio-temporal edge aggregation mechanism that combines temporal verification of spatial nodes, spatial intersection computation, and temporal intersection analysis to achieve spatio-temporal filtering.
