Enhancing privacy-preserving machine learning with self-learnable activation functions in fully homomorphic encryption

Abstract

In the field of artificial intelligence and data engineering, the effective utilization of data is critical for improving productivity across various sectors. As machine learning increasingly relies on sensitive data, balancing privacy with computational efficiency has become a major challenge. Homomorphic encryption provides a promising solution by enabling computation on encrypted data while preserving privacy in machine learning. However, its integration with neural networks is hindered by high computational demands and limitations in performing complex nonlinear operations. To address these challenges, we propose a novel approach that incorporates a ”Self-Learnable Activation Function” (SLAF) and refines the structure of neural network linear layers. These enhancements are designed to accommodate the constraints of homomorphic encryption, allowing for deeper network architectures without significant computational overhead.

Our optimized neural network model, tailored for biometric authentication tasks, outperforms traditional methods that use simple polynomial activation functions. Using the UTKFace dataset, which includes facial features under diverse scenarios, we validated the effectiveness of our solution in real-world applications. Experimental results demonstrate accuracy improvements of 0.88% to 3.15% over traditional models and 4.87% to 9.67% over the CryptoNets model, underscoring the capability of our approach to meet stringent privacy-preserving biometric authentication requirements.