With the rise of cloud computing and distributed machine learning, the protection of data privacy during computation has become a critical challenge. Homomorphic Encryption (HE) offers a solution by allowing computations on ciphertexts, generating encrypted results that, when decrypted, match the result of operations on plaintexts. However, the adoption of HE is often hindered by complexity of implementation and computational overhead. This paper presents an analysis of , an open-source framework hosted on GitHub. We explore the architecture of HEU, its integration within the SecretFlow ecosystem, its unique "Semi-Homomorphic" optimization strategies, and its viability for real-world applications in privacy-preserving data science.