: We study the problem of enabling users to perform range queries over a cloud-managed database without revealing which records are retrieved, while also allowing users to verify the correctness of the returned results. Existing approaches support either query privacy through Private Information Retrieval (PIR) or query result authentication through Authenticated Data Structures (ADSs), but not both simultaneously for range queries. To address this problem, we present an Authenticated Private Information Retrieval (APIR) protocol for range queries with guarantees of soundness and completeness. We first propose a baseline scheme that combines PIR with Merkle Hash Trees (MH-trees), achieving authenticated query processing but incurring high communication and storage overhead due to redundant verification objects (VOs). To improve efficiency, we further introduce the APIR-tree , a novel authenticated data structure that embeds values at every node, enabling nodes to serve as VOs for their ancestors and eliminating redundancy. Simulation results show that the APIR-tree reduces storage overhead by up to 25 × and communication costs by an order of magnitude while preserving strong privacy and authenticity guarantees.
Priority scheduling techniques aim to schedule tasks with higher priority and tighter deadlines to be executed before other tasks. However, traditional blockchain systems do not work well with priority scheduling due to issues regarding consensus mechanisms, throughput, and scalability. In this work, we propose three transaction scheduling schemes which employ a fee-based model to differentiate priority levels of transactions and select transactions for validation and inclusion based on criteria other than the order of arrival. The Priority-Based Scheduling scheme focuses on the priority level of individual transactions, the Contract-Based Scheduling scheme emphasizes the satisfaction of more customer contracts, whereas the Latency-Based Scheduling scheme prioritizes transactions with closer deadlines when bursts of transactions overwhelm the blockchain network. We implement prototypes of our transaction scheduling schemes in a private Algorand-inspired simulation and evaluate their performance by testing them under scenarios representing different transaction distribution ratios. The results show that the three transaction scheduling schemes outperform the baseline in which no scheduling is applied, and each scheme performs best in the transaction distribution scenario it is designed to handle.
Matthew Sharp, Chin-Tser Huang, Soamar Homsi· International Conference on...· 0 citations
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