Homomorphic encryption (HE) is a cryptographic technique that allows computations to be performed on encrypted data without decrypting it. This technology has the potential to address privacy concerns when outsourcing data storage and processing to cloud services. However, the practicality of HE depends on its efficiency, as current fully homomorphic encryption (FHE) schemes are computationally intensive and not yet efficient enough for widespread use. The paper explores the feasibility of HE by examining real-world applications that require only "somewhat" homomorphic encryption (SwHE), which supports a limited number of homomorphic operations. SwHE schemes are more efficient and compact than FHE schemes, making them suitable for practical applications in domains such as medicine, finance, and advertising. The authors present a proof-of-concept implementation of a SwHE scheme based on the "ring learning with errors" (Ring LWE) problem, which is secure and efficient. Their implementation, using the MAGMA system, demonstrates that SwHE can be as efficient as optimized pairing-based schemes with the same level of security. The paper also discusses practical applications of HE, such as computing statistical functions and predictive models on encrypted medical data, performing private financial computations, and targeted advertising based on encrypted user data. These applications highlight the potential of HE to enable secure and private data processing in cloud environments. The authors also describe the parameters and performance of their HE scheme, including key and ciphertext sizes, encryption and decryption times, and the efficiency of homomorphic operations. They note that while the current implementation is efficient, further optimizations could improve performance. The paper concludes by discussing future work, including the implementation of fully homomorphic encryption and the optimization of communication with cloud services.Homomorphic encryption (HE) is a cryptographic technique that allows computations to be performed on encrypted data without decrypting it. This technology has the potential to address privacy concerns when outsourcing data storage and processing to cloud services. However, the practicality of HE depends on its efficiency, as current fully homomorphic encryption (FHE) schemes are computationally intensive and not yet efficient enough for widespread use. The paper explores the feasibility of HE by examining real-world applications that require only "somewhat" homomorphic encryption (SwHE), which supports a limited number of homomorphic operations. SwHE schemes are more efficient and compact than FHE schemes, making them suitable for practical applications in domains such as medicine, finance, and advertising. The authors present a proof-of-concept implementation of a SwHE scheme based on the "ring learning with errors" (Ring LWE) problem, which is secure and efficient. Their implementation, using the MAGMA system, demonstrates that SwHE can be as efficient as optimized pairing-based schemes with the same level of security. The paper also discusses practical applications of HE, such as computing statistical functions and predictive models on encrypted medical data, performing private financial computations, and targeted advertising based on encrypted user data. These applications highlight the potential of HE to enable secure and private data processing in cloud environments. The authors also describe the parameters and performance of their HE scheme, including key and ciphertext sizes, encryption and decryption times, and the efficiency of homomorphic operations. They note that while the current implementation is efficient, further optimizations could improve performance. The paper concludes by discussing future work, including the implementation of fully homomorphic encryption and the optimization of communication with cloud services.