Blockchain Segmentation: An Industrial Solution for Large Scale Data

Blockchain Segmentation: An Industrial Solution for Large Scale Data

연구 분야: Databases

학회: International Conference on Soft Computing and its Engineering Applications

Blockchain technology finds diverse applications, encompassing the security and managing the vast amounts of data generated by IoT devices, with secure and transparent communication between them. In Supply chain management, blockchain can track products and goods from their origin to destination. A blockchain is an autonomous digital ledger employing Distributed Ledger Technology (DLT) to securely and transparently record transactions in a decentralized manner. It accomplishes this by using a network of computers (nodes) and these components collaborate to verify and register transactions within a communal database. This research proposes a new approach called Segment Blockchain that divides a blockchain into smaller segments and enables nodes to retain only one segment in place of the entire blockchain. This approach can potentially reduce the storage requirements for participating nodes, facilitate the incorporation of addional nodes into the network and maintain a copy of the blockchain. Our proposed methodology aims to address the concern of the risk of a singular vulnerable point, wherein a malicious entity keeps all copies of a specific segment and leaves the system, causing in the irreversible deletion of that segment. The proposed blockchain system can handle big data while reducing storage space demands while ensuring heightened security for user data. Theoretical evidence shows that this is achieved by limiting the number of blocks a malicious entity has the capability to both store and distribute every segment over a cluster of cloud-based blocks, the storage burden is significantly reduced compared to conventional designs. The system was successful in reducing storage space by 33% for large scale data. This makes the proposed segmentation approach more practical for processing and managing large volumes of data.