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Deep learning simulation necessitates a considerable amount of internal computational resources and fast training for large amounts of data. The cloud has been delivering software to help with this transition in recent years, posing additional security risks to data breaches. Modern encryption schemes maintain personal secrecy and are the best method for protecting data stored on a server and data sent from an unauthorized third party. However, when data must be stored or analyzed, decryption is needed, and homomorphic encryption was the first symptom of data security issues found with Strong Encryption.It enables an untrustworthy cloud resource to process encrypted data without revealing sensitive information. This paper looks at the fundamental principles of homomorphic encryption, their forms, and how to integrate them with deep learning. Researchers are particularly interested in privacy-preserving Homomorphic encryption schemes for neural networks. Finally, present options, open problems, threats, prospects, and new research paths are identified across networks