Survey: Homomorphic Encryption-based Deep Learning that Preserves Privacy

When deep learning methods are effective, the volume of information accessible for training is expanding quickly, with large-scale user data collection in businesses taking the lead. Due to the sensitive nature of user data and the way. Data collection creates privacy concerns because of the potential for this material (images and audio recordings) to be preserved forever. The concepts of privacy and secrecy pertain to the avoidance of sharing this data, and a higher volume of data is required for deep learning to be achieved. When data must be accessed for training, machine-learning algorithms face certain difficulties. Several privacy-preserving deep learning solutions, such as multi-lateral computation secrecy and symmetric encryption within neural networks, have been developed to address these concerns.