Hybrid Encryption Models for Optimal Balance of Security, Scalability, and Computational Efficiency in Cloud Computing

As cloud computing continues to evolve and manage increasingly large and diverse datasets, achieving a balance between security, scalability, and computational efficiency remains a critical challenge. This study presents a comparative evaluation of four hybrid encryption models, Rivest-Shamir-Adleman-Advanced Encryption Standard (RSA-AES), Elliptic Curve Cryptography-Advanced Encryption Standard (ECC-AES), RSA-Blowfish, and ECC-Blowfish, designed to integrate the strengths of symmetric and asymmetric cryptography. Each model is implemented using robust configurations, including AES-256-Galois/Counter Mode, Blowfish-EAX, RSA-3072, ECC Curve25519, and SHA3-256 hashing. Experimental analysis was conducted using multimodal datasets comprising text, image, audio, and tabular files with sizes ranging from 10 MB to over 1 GB to reflect real-world cloud storage conditions. Performance metrics such as encryption/decryption time, throughput, and cryptographic randomness were measured. Statistical significance testing and the NIST Statistical Test Suite were applied to evaluate consistency and cipher strength. Results indicate that ECC-AES achieved the highest throughput and most stable randomness characteristics across file types, while RSA-AES showed increased cryptographic robustness with larger data sizes. Although Blowfish-based hybrids ensured strong randomness, their slower performance makes them less suited for high-throughput cloud environments. The findings support ECC-AES as the optimal hybrid encryption model for cloud computing, offering a strong trade-off between security and operational efficiency.