关键词: 全同态加密, 隐私保护, 两方协同, 语音分类, 安全计算协议

Abstract: To address the challenges of low computational efficiency and classification accuracy in existing fully homomorphic encryption technology for speech classification tasks, a high-precision and scalable encrypted privacy-preserving speech classification (PPSC) scheme is proposed. First of all, A secure multiplication protocol based on CKKS fully homomorphic encryption technology is designed to avoid the use of expensive bootstrapping operations, which can effectively improve the computational efficiency of deep ciphertext multiplication, so that the scheme can be extended to deeper neural networks. Based on the above architecture, secure non-polynomial protocols such as secure exponent, secure reciprocal and secure comparison are designed. Compared with the method of polynomial approximate fitting of non-polynomial operations, our protocols improve computation accuracy and reduces computation overhead. Secondly, the PPSC scheme securely implements the fundamental modules such as the convolutional layer, ReLU layer, average pooling layer, fully connected layer, and Softmax layer. This ensures the privacy of speech data, speech classification models, and intermediate computing results. Finally, a detailed theoretical analysis of the PPSC scheme is conducted to evaluate its effectiveness and security. The analysis demonstrates that the secure multiplication protocol exhibits higher computational efficiency in deeper multiplication operations. Experimental results on the Speech Command Database validate the effectiveness of the PPSC scheme in achieving accurate speech classification while preserving the privacy of data and model parameters. Furthermore, the proposed scheme achieves an accuracy that is 3.57% higher than that of the HEKWS scheme.

Key words: Fully Homomorphic Encryption, Privacy-Preserving, Two-Party Collaboration, Speech Classification, Secure Computing Protocol