Abstract

Cloud computing has revolutionized data storage and processing, offering scalability, flexibility, and cost efficiency. However, security and privacy concerns remain significant challenges, particularly when sensitive data is stored and processed by third-party cloud providers. Traditional encryption techniques, such as AES and RSA, ensure data confidentiality but require decryption for computation, exposing data to potential breaches. This review presents a novel approach to cloud data encryption using Homomorphic Encryption (HE), which enables computations on encrypted data without requiring decryption. Our approach leverages Fully Homomorphic Encryption (FHE) to facilitate secure data processing in cloud environments while preserving confidentiality. Unlike conventional encryption schemes, which restrict operations on encrypted data, HE allows mathematical functions to be executed directly on ciphertexts, producing encrypted results that can be decrypted to obtain accurate outputs. This capability is particularly useful in privacy-sensitive applications such as healthcare, finance, and artificial intelligence, where outsourced data processing must remain confidential. The proposed system integrates optimized HE algorithms to reduce computational overhead, addressing one of the key challenges in HE adoption. We evaluate the security and performance of our approach by implementing a case study on encrypted data analytics in a cloud environment. Our experimental results demonstrate that while HE introduces computational complexity, recent advancements in hardware acceleration and algorithm optimization significantly enhance its feasibility for real-world applications. This highlights the potential of homomorphic encryption as a transformative solution for secure cloud computing. By enabling privacy-preserving computations, our approach ensures data confidentiality while leveraging the full power of cloud computing. Future research will focus on improving efficiency, scalability, and hybrid cryptographic models to further enhance security in cloud-based ecosystems.