International Association
for Cryptologic Research

Energy-efficient edge devices are essential for the widespread deployment of machine learning (ML) services. However, their limited computational capabilities make local model training infeasible. While cloud-based training offers a scalable alternative, it raises serious privacy concerns when sensitive data is outsourced. Homomorphic Encryption (HE) enables computation directly on encrypted data and has emerged as a promising solution to this privacy challenge. Yet, current HE-based training frameworks face several shortcomings: they often lack support for complex models and non-linear functions, struggle to train over multiple epochs, and require cryptographic expertise from end users.

We present HE-SecureNet, a novel framework for privacy-preserving model training on encrypted data in a single-client–server setting, using hybrid HE cryptosystems. Unlike prior HE-based solutions, HE-SecureNet supports advanced models such as Convolutional Neural Networks and handles non-linear operations including ReLU, Softmax, and MaxPooling. It introduces a level-aware training strategy that eliminates costly ciphertext level alignment across epochs. Furthermore, HE-SecureNet automatically converts ONNX models into optimized secure C++ training code, enabling seamless integration into privacy-preserving ML pipeline—without requiring cryptographic knowledge.

Experimental results demonstrate the efficiency and practicality of our approach. On the Breast Cancer dataset, HE-SecureNet achieves a 5.2× speedup and 33% higher accuracy compared to ConcreteML (Zama) and TenSEAL (OpenMined). On the MNIST dataset, it reduces CNN training latency by 2× relative to Glyph (Lou et al., NeurIPS’20), and cuts communication overhead by up to 66× on MNIST and 42× on CIFAR-10 compared to MPC-based solutions.