中圖分類號：TP309文獻標識碼：ADOI:10.19358/j.issn.2097-1788.2025.11.002引用格式：張恒，廖尚斌，張陳穎. 基于同態加密的AI模型參數安全計算與防泄露方法［J］.網絡安全與數據治理，2025，44（11）：7-11,17.

Secure computation and anti-leakage methods for AI model parameters based on homomorphic encryption

Abstract： With the extensive application of artificial intelligence in sensitive fields such as healthcare and finance, the privacy protection of model parameters and training data has become a critical issue. This paper proposes a secure computation and antileakage method for AI model parameters based on homomorphic encryption (HE). The method employs the CKKS scheme to implement parameter encryption, forward inference, and gradient updates in the ciphertext space, thereby avoiding the risk of plaintext exposure during training. The results demonstrate that HESGD achieves a maximum accuracy of 99.1% on MNIST. In terms of computational overhead, it balances efficiency and security, with an information leakage risk index close to 0.0. The study indicates that the proposed method maintains model precision while achieving efficient and secure computation with nearly zero leakage risk, showing strong application value.

Key words : model parameters; privacy protection; homomorphic encryption; CKKS scheme; gradient updates