CONCLUSION
The most important factors affecting performance in learning-based systems are the number of data and data diversity. In running learning-based systems at distributed points in a system, accuracy performance may not reach the desired level due to data set diversity and insufficient amount of data. Collecting, storing and training data in a central location brings certain risks and precautions.
When the features of the infrastructure proposed in this study are compared with the studies carried out in the literature, Table 6 shows the advantages of our infrastructure. GDPR policies, log immutability, storage strategy, edge computing system prepared for network performance stand out as important positive differences. In addition, basic features such as data security, non-tamperability, distributed structure, and identification, which are also present in other studies, are also available in our infrastructure.
Table 6. Comparison of the use of Blockchain to secure data in the cloud ecosystem.