HHEML: Hybrid Homomorphic Encryption for Privacy-Preserving Machine Learning on Edge

Privacy-preserving machine learning (PPML) demands secure inference on sensitive data in untrusted environments. Fully homomorphic encryption (FHE) enables computation directly on encrypted data, but it incurs prohibitive communication and computational overhead on edge devices. Hybrid homomorphic encryption (HHE) mitigates this by combining symmetric encryption with FHE, reducing client-side costs; however, existing implementations remain impractical for resource-constrained deployments. This paper proposes the first end-to-end hardware-accelerated HHE framework, which integrates a lightweight symmetric cipher optimized for FHE compatibility with a dedicated hardware accelerator. Beyond this integration, we introduce a microarchitectural optimization for throughput and energy efficiency. The proposed architecture is validated within a complete PPML pipeline, demonstrating significantly lower latency and power consumption than software implementations. Experimental results on a PYNQ-Z2 platform with MNIST validate our approach, achieving a 50× reduction in client-side encryption latency and a 2× throughput gain over existing FPGA-based HHE accelerators. These improvements enable practical and secure inference on edge devices. Our contributions establish a hardware-software co-design methodology for deploying scalable, secure machine learning in resource-constrained environments, validating the feasibility of low-power HHE for edge deployment.