CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference. (arXiv:2209.11904v3 [cs.CR] UPDATED)

Recently cloud-based graph convolutional network (GCN) has demonstrated great
success and potential in many privacy-sensitive applications such as personal
healthcare and financial systems. Despite its high inference accuracy and
performance on cloud, maintaining data privacy in GCN inference, which is of
paramount importance to these practical applications, remains largely
unexplored. In this paper, we take an initial attempt towards this and develop
$\textit{CryptoGCN}$--a homomorphic encryption (HE) based GCN inference
framework. A key to the success of our approach is to …

encrypted fast network