PPGNN: Fast and Accurate Privacy-Preserving Graph Neural Network Inference via Parallel and Pipelined Arithmetic-and-Logic FHE Accelerator

Graph Neural Networks (GNNs) are increasingly used in fields like social media and bioinformatics, promoting the prosperity of cloud-based GNN inference services. Nevertheless, data privacy becomes a critical issue when handling sensitive information. Fully Homomorphic Encryption (FHE) enables computations on encrypted data, while privacy-preserving GNN inference generally necessitates ensuring graph structure data confidentiality and maintaining computation precision, both of which are computationally expensive in FHE. Existing schemes of GNNs inference with FHE are deterred by either computational overhead, accuracy degradation, or incomplete data protection. This paper presents PPGNN to address these challenges all at once. We first propose a novel privacy-preserving GNN inference algorithm utilizing a high-accuracy arithmetic-and-logic FHE approach, meanwhile only need much smaller parameters, substantially reducing computational complexity and facilitating parallel processing. Correspondingly, a dedicated hardware architecture has been designed to implement these innovations, with featured specialized units for arithmetic and logic FHE operations in a pipelined manner. Collectively, PPGNN achieves 2.7× and 1.5× speedup over state-of-the-art Arithmetic FHE and Logic FHE accelerators while ensuring high accuracy, simultaneously with about 18× energy reduction on average.