Research on the adaptability of homomorphic encryption schemes in deep learning applications

With the rapid development of information technology, data security and privacy protection have become increasingly prominent key issues. This paper mainly focuses on the adaptation of homomorphic encryption schemes in deep learning applications. Firstly, the theoretical foundation of homomorphic encryption is expounded, including the encryption principle of the homomorphic encryption algorithm. Then, the performance, advantages and disadvantages of the current mainstream homomorphic encryption schemes are deeply compared to select the most suitable homomorphic encryption scheme for deep learning. Finally, the paper summarizes and puts forward some deficiencies of the homomorphic encryption scheme in the field of deep learning.