International Association
for Cryptologic Research

Fully Homomorphic Encryption (FHE) enables computations on encrypted data without requiring decryption. However, each computation increases the noise level, which can eventually cause decryption failures once a certain threshold is reached. In particular, homomorphic multiplication significantly amplifies noise in the ciphertext. In this work, we revisit Ring-learning-With-Error (RLWE) based encryption proposed by Fan et al. and present an optimized noise growth approach by swapping the sample space for secret key and error distribution. Thereafter, we revisit BFV homomorphic multiplication proposed by Kim et al. (ASIACRYPT'21) and present an optimized noise bound. Later, we empirically check the hardness of proposed scheme using lattice estimator. Our analysis demonstrates that the proposed method achieves more than 128-bit security and achieves a lower noise bound than existing techniques.