<![CDATA[Fully Homomorphic Encryption (FHE) allows computation on ciphertext without decryption to provide confidentiality in privacy-critical use cases. However, its practical adoption remains limited due to very high computational overhead, scalability limitations, and susceptibility to attacks. This paper introduces an efficient hybrid FHE platform that integrates fast bootstrapping, key-switching optimization, and post-quantum security. The objectives are to: (1) minimize bootstrapping overhead to enhance computation efficiency; (2) optimize key-switching operations to improve scalability in encrypted computation; (3) enhance security by incorporating post-quantum cryptographic protocols such as lattice-based encryption; and (4) evaluate the feasibility of cloud computing, federated learning, and encrypted AI applications. To achieve these objectives, this work extends state-of-the-art FHE schemes (BFV, CKKS, and TFHE) by simplifying bootstrapping with lower computational cost, employing batch encryption methods to improve scalability, incorporating post-quantum security measures to resist quantum attacks, using Python-based deployments to verify practicality in real-world cloud security, federated learning, and encrypted AI inference, and enhancing quantum resistance through lattice-based key-switching methods. This paper also introduces an updated version of the Microsoft SEAL CKKS scheme that incorporates optimized bootstrapping parameters, improved key-switching, and SIMD-based parallelism. The proposed enhancements are benchmarked against the baseline Microsoft SEAL CKKS implementation, demonstrating considerable improvements in speed, memory efficiency, and security. Experimental results show a 36% reduction in bootstrapping time, a 36% improvement in key-switching evaluation, a 40% reduction in memory usage, and a 31% reduction in ciphertext size compared with the baseline SEAL CKKS implementation, together with a 15× improvement in resistance to lattice-reduction attacks and a 6× reduction in side-channel vulnerability. Overall, the proposed framework significantly improves the efficiency, scalability, and security of FHE, making it more suitable for practical real-world applications. Future work will explore hardware acceleration and adaptive encryption to further improve performance.]]>
