被撤回的出版物: Towards secure deep learning architecture for smart farming-based applications (Retracted article. See DEC, 2022)

The immense growth of the cloud infrastructure leads to the deployment of several machine learning as a service (MLaaS) in which the training and the development of machine learning models are ultimately performed in the cloud providers' environment. However, this could also cause potential security threats and privacy risk as the deep learning algorithms need to access generated data collection, which lacks security in nature. This paper predominately focuses on developing a secure deep learning system design with the threat analysis involved within the smart farming technologies as they are acquiring more attention towards the global food supply needs with their intensifying demands. Smart farming is known to be a combination of data-driven technology and agricultural applications that helps in yielding quality food products with the enhancing crop yield. Nowadays, many use cases had been developed by executing smart farming paradigm and promote high impacts on the agricultural lands.

Automated summary

This paper primarily focuses on developing a secure deep learning system design within smart farming technologies, as they are paying more attention to global food supply needs. Smart farming combines data-driven technology and agricultural applications, helping to increase crop yield.