Homomorphic Encryption (HE) enables computations to be performed directly on encrypted data, providing strong cryptographic security guarantees and preserving data confidentiality in untrusted environments such as cloud computing, high-performance computing, and machine learning platforms. Among existing HE schemes, Cheon-Kim-Kim-Song (CKKS) enables efficient approximate arithmetic over encrypted real-valued data, making it particularly well suited for numerical workloads. However, its practical deployment remains constrained by the substantial computational cost of its core ciphertext operations. In this work, we present a software-based acceleration of CKKS arithmetic kernels on ARM Neoverse processors through Single Instruction, Multiple Data (SIMD) vectorization using the Scalable Vector Extension (SVE). Our approach targets the widely adopted Microsoft SEAL library and introduces SVE-optimized implementations of the key polynomial-arithmetic primitives underlying CKKS ciphertext addition, multiplication, and rotation. By leveraging data-level parallelism, vector-length-agnostic programming, and microarchitectural features of the target processor, the proposed optimizations enhance execution efficiency while preserving functional correctness and compatibility with the existing library interface. Experimental evaluation on an ARM Neoverse V2 platform (NVIDIA Grace processor) demonstrates performance improvements of 8–14% for ciphertext multiplication and rotation, and approximately 30% for addition, across multiple CKKS parameter configurations. These results highlight the potential for more efficient processor-only execution of homomorphic encryption workloads through architecture-aware optimization.
