A hybrid architecture for secure Big-Data integration and sharing in Smart Manufacturing

Big-Data integration and sharing is a key aspect of the manufacturing industry, as it enables real-time monitoring and control of production processes. However, integrating and sharing Big-Data also raises concerns about performance, privacy, and security. This study presents a hybrid architecture for integrating and sharing Big-Data in the manufacturing industry, with a focus on addressing performance, privacy, and security concerns. The proposed architecture combines Apache Spark, IPFS, and Blockchain technology, and is based on a sharding approach that encrypts and distributes data to enable faster and more efficient data transfer. The Blockchain network is used to provide a secure and tamper-proof record of the data transfer, ensuring that the data is only accessible to authorized parties and that any changes are visible to all parties on the network. While the proposed architecture includes a verification method to meet quality requirements, only the uploading and downloading processes were validated through specification.