| Title | Blockchain-Based Distributed Data Integrity Auditing Scheme |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Baofu, Han, Hui, Li, Chuansi, Wei |
| Conference Name | 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA) |
| Keywords | Big Data, blockchain, cloud computing, component, compositionality, Conferences, data integrity, decentralization, fair arbitration, Predictive Metrics, provable security, pubcrawl, public auditing, Resiliency, Resists, security, smart contracts |
| Abstract | Cloud storage technology enables users to outsource local data to cloud service provider (CSP). In spite of its copious advantages, how to ensure the integrity of data has always been a significant issue. A variety of provable data possession (PDP) scheme have been proposed for cloud storage scenarios. However, the participation of centralized trusted third-party auditor (TPA) in most of the previous work has brought new security risks, because the TPA is prone to the single point of failure. Furthermore, the existing schemes do not consider the fair arbitration and lack an effective method to punish the malicious behavior. To address the above challenges, we propose a novel blockchain-based decentralized data integrity auditing scheme without the need for a centralized TPA. By using smart contract technique, our scheme supports automatic compensation mechanism. DO and CSP must first pay a certain amount of ether for the smart contract as deposit. The CSP gets the corresponding storage fee if the integrity auditing is passed. Otherwise, the CSP not only gets no fee but has to compensate DO whose data integrity is destroyed. Security analysis shows that the proposed scheme can resist a variety of attacks. Also, we implement our scheme on the platform of Ethereum to demonstrate the efficiency and effectiveness of our scheme. |
| DOI | 10.1109/ICBDA51983.2021.9403079 |
| Citation Key | baofu_blockchain-based_2021 |
Blockchain-Based Distributed Data Integrity Auditing Scheme