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<p>Fully Homomorphic Encryption (FHE) enables computation over encrypted data while preserving strong security and privacy guarantees. However, its high computational cost remains a major challenge. This study therefore evaluates the performance of homomorphic matrix multiplication using three Homomorphic Encryption (HE) libraries, Microsoft SEAL, HElib and OpenFHE, across two platforms with AMD EPYC and Intel Xeon CPUs, with a particular focus on the impact of different compiler flags. The results indicate that compiler configurations and hardware selection significantly influence runtime in libraries such as Microsoft SEAL, whereas HElib and OpenFHE show negligible variation under different compilation settings. Furthermore, the analysis reveals that SEAL makes more efficient use of memory bandwidth while OpenFHE achieves the highest overall performance, the lowest mean absolute error and the shortest execution time.</p>

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Cross-platform characterisation and performance analysis of homomorphic matrix multiplication

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月10日

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  • Franklin Espinoza,
  • Justo Molina,
  • Darwin Quezada-Gaibor,
  • Sandra Catalán,
  • Manuel F. Dolz

摘要

Fully Homomorphic Encryption (FHE) enables computation over encrypted data while preserving strong security and privacy guarantees. However, its high computational cost remains a major challenge. This study therefore evaluates the performance of homomorphic matrix multiplication using three Homomorphic Encryption (HE) libraries, Microsoft SEAL, HElib and OpenFHE, across two platforms with AMD EPYC and Intel Xeon CPUs, with a particular focus on the impact of different compiler flags. The results indicate that compiler configurations and hardware selection significantly influence runtime in libraries such as Microsoft SEAL, whereas HElib and OpenFHE show negligible variation under different compilation settings. Furthermore, the analysis reveals that SEAL makes more efficient use of memory bandwidth while OpenFHE achieves the highest overall performance, the lowest mean absolute error and the shortest execution time.