A Scalable and Timely Data Auditing Scheme for Metaverse
Chen Chen, Tong Wu, Chao Ren, Chang‐ai Sun · 2024
The metaverse is a new trend in virtual reality applications, and data storage and management commonly rely on distributed storage systems. The integrity of the stored data has become a key concern in the metaverse. Furthermore, the scalability of data storage is a significant characteristic of the metaverse. Therefore, it is essential to provide a scalable data auditing approach for distributed outsourcing storage systems. However, the existing data auditing schemes do not fully consider the timeliness of auditing, which is a shred of important evidence for users to ensure the storage status. In this paper, we propose a scalable data auditing scheme with timeliness, which supports scalable data storage and timestamp updates. By leveraging the blockchain infrastructure, our scheme ensures the fairness and transparency of the data auditing process in a distributed storage system. To achieve efficiency in scalable data auditing, our scheme uses pseudo-indexed linked lists by ordinary arrays to store the relevant information needed for data auditing, which can effectively support resource-constrained blockchain for on-chain auditing. Finally, we analyze the computational cost, communication overhead, and storage overhead of the scheme theoretically, verify the correctness of the scheme and compare the efficiency of the key algorithms experimentally.