Optimized Secure Federated Learning for Event Detection in Big Data using Blockchain Mechanism

Currently, cloud storage in blockchain and federated learning technology provides better security among data transmission and file access. But, in some of the cases, security issues arose. So, to avoid security problems and offer better protection in a cloud environment, a novel optimized buffalo-based Homomorphic SHA blockchain (OBHSB). In this model for accessing the cloud storage data with the key matching method, if any of the unauthenticated users are trying to access the file initially, the system checks the key matching parameter. The proposed model was developed to provide better security in big data presented in the cloud environment. However, the parameters in the proposed model were compared with the existing models to make sure better performance was attained through the proposed model. Attack was considered as an event in this research. In the performance analysis, the performance rate of the proposed model was validated. Subsequently, the case study was developed in this research to explain the working procedure of the proposed design; model performs hashing, encryption, decryption, and key matching mechanisms. The results proposed model is observed to have 100% confidentiality rate after attack.

@article{Lakshmi2023,
  title     = {Optimized Secure Federated Learning for Event Detection in Big Data using Blockchain Mechanism},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {5},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {K. Prasanna Lakshmi and K. Swapnika},
  doi       = {10.14569/IJACSA.2023.0140541},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140541}
}