Treffer: BE-DSN: leveraging blockchain for improving data availability and security in distributed storage networks.

In the age of Industry 4.0 and the Internet of Things (IoT), a massive amount of data is produced by digital applications. Since organizations process this data to extract business strategies, it requires a reliable and secure storage platform. Conventional centralized storage solutions based on cloud and data centers, despite being efficient, undergo transparency and trust issues because of centralized management. To deal with these concerns, distributed storage systems have emerged due to the reputation of blockchain technologies and the distributed web. The uploaded files to these systems are distributed among a network of untrusted and heterogenous peers, leading to various challenges, particularly data security and availability. This study presents BE-DSN, a novel distributed storage network employing the Hybrid Encoding and Replication Network (HBERN) for improving file availability and the Dynamic Key Management and Distributed Encryption Network (DKMEN) for multilayered data security and integrity persistence. BE-DSN also leverages blockchain technology to implement on-chain metadata storage using smart contracts for reliable accessibility and immutability. We evaluated the BE-DSN in real-world scenarios for various chunk loss circumstances on a cluster running the InterPlanetary File System (IPFS) network. The evaluations demonstrate that at a chunk loss rate of 50%, BE-DSN expresses a probability of data loss around 1%, compared to 16.7% of Snarl, a state-of-the-art system. The system guarantees data security and integrity by employing DKMEN, a chunk-level multi-layered encryption mechanism, with each chunk encrypted independently applying a unique encryption key, protecting against security vulnerabilities. Thus, BE-DSN presents an effective solution to alleviate the key issues of data security and availability in distributed storage systems. [ABSTRACT FROM AUTHOR]

Copyright of Cluster Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)