This study aims to compare the security and efficiency of keyless encryption and decryption techniques for big data processing. With the increasing amount of data being generated and processed in today’s data-driven world, maintaining data security and privacy has become a major concern. Keyless encryption techniques, including homomorphic encryption (HE) and fully homomorphic encryption (FHE), have gained significant attention for their ability to provide strong security while maintaining data privacy. The literature review conducted in this study highlights the advantages and limitations of keyless encryption techniques and emphasizes the importance of selecting the appropriate key generation method and algorithm for ensuring security and efficiency. The study also provides a basic algorithm outline for implementing keyless encryption and decryption using homomorphic encryption (HE). While keyless encryption methods such as fully homomorphic encryption (FHE) and homomorphic encryption (HE) provide robust data protection, they also present computing difficulties. Their resource requirements may make them inaccessible to smaller enterprises. To increase scalability and efficiency for real-world use in big data applications, more research is required. Overall, the study aims to provide a better understanding of the benefits and challenges of keyless encryption techniques for big data processing and to assist researchers and practitioners in selecting the appropriate encryption technique for their specific use case.