Towards Efficient Verifiable Cloud Storage and Distribution for Large-Scale Data Streaming
Haining Yang, Dengguo Feng, Jing Qin · IEEE Transactions on Parallel and Distributed Systems · 2025
Data streaming is an ordered sequence of data continuously generated over time, whose dynamic scale is hard to be predicated in advance. Since the traditional integrity verification primitives are not qualified to check the integrity of the retrieved data and the outsourced database in streaming setting, some specific schemes were proposed by adopting the tree- like authentication structure or the combination of signature and accumulator. However, these schemes are not optimal for the owner. The main concerns can be generalized as how to reduce the size of the authentication information to be less than the scale of the data streaming, and enable the resource-constrained owner to check the data integrity without using challenge. To address the problems, we intend to find a new approach to design the scheme by exploiting the novel technique called decentralized vector commitment (DVC). Towards this goal, we first propose a key exposure-freeness chameleon vector commitment scheme, and then present the efficient DVC technique based on our key exposure-freeness chameleon vector commitment scheme. The scheme is finally constructed by leveraging the efficient DVC technique. Besides the integrity verification, our scheme is also sufficient to efficiently distribute the data to a user who is protected from receiving the stale data. To optimize the performance in concurrently retrieving multiple data, we introduce the batch query that reduces large amounts of communication and computation overheads. The security analysis and performance evaluation show that our solutions are secure and efficient.