Blockchain-Assisted Searchable Integrity Auditing for Large-Scale Similarity Data With Arbitration

Ying Miao, Keke Gai*, Yu An Tan, Liehuang Zhu, Weizhi Meng

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

Data integrity auditing technology serves as an essential tool to ensure the data's integrity with the popularity of remote storage. However, existing data integrity auditing models are unsuitable for a large number of files with interrelationships and heavily depend on a centralized Third-Party Auditor (TPA). To address these issues, in this paper we propose a blockchain-assisted searchable integrity auditing scheme for large-scale similarity data. To broaden the scope of the auditing model and enhance its ability to handle interconnected files, we utilize the keyword to design a search index and a trapdoor to achieve authenticator searchability for the interconnected files. The integrity of the searching result from the cloud side can be guaranteed at the same time. To reduce reliance on centralized TPA and enhance the credibility and transparency of auditing, we integrate blockchain technology along with smart contracts to replace TPA and achieve multitask auditing. We adopt a certificateless cryptosystem to generate the authenticator, while considering the cost reduction. Moreover, an arbitrator is proposed to achieve fairness judge. Theoretical and security analysis demonstrate that the proposed scheme is efficient and secure, making it a promising solution for data auditing in a wide range of applications.

源语言英语
期刊IEEE Transactions on Dependable and Secure Computing
DOI
出版状态已接受/待刊 - 2025
已对外发布

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