An ML Bio-inspired Model for improving Security and Speed of FHE for Cybersecurity

Fully homomorphic encryption is the scheme, which allows the users to perform arbitrary computation on encrypted data without decrypting it. Input data can be encrypt ,while the another user, without having decryption key can carry out data processing on the encrypted input. The encrypted output can be recovered by the user having the secret key. ML is one among the foremost exciting technologies that one would have ever encountered. Optimization is an important and crucial a part of machine learning, has attracted researchers attention. Applying the idea of Genetic Algorithms and Machine learning on Cipher system provides much more security for the ciphertext data. In this paper, we implement BGV Fully Homomorphic Encryption (FHE) scheme. Then, we introduce ML based Genetic Algorithm Model to enhance security of BGV FHE.