Efficient Single-Server Private Inference Outsourcing for Convolutional Neural Networks

Private inference outsourcing ensures the privacy of both clients and model owners when model owners deliver inference services to clients through third-party cloud servers. Existing solutions either reduce inference accuracy due to model approximations or rely on the unrealistic assumption of non-colluding servers. Moreover, their efficiency falls short of HELiKs, a solution focused solely on client privacy protection. In this paper, we propose Skybolt, a single-server private inference outsourcing framework without resorting to model approximations, achieving greater efficiency than HELiKs. Skybolt is built upon efficient secure two-party computation protocols that safeguard the privacy of both clients and model owners. For the linear calculation protocol, we devise a ciphertext packing algorithm for homomorphic matrix multiplication, effectively reducing both computational and communication overheads. Additionally, our nonlinear calculation protocol features a lightweight online phase, involving only the addition and multiplication on secret shares. This stands in contrast to existing protocols, which entail resource-intensive techniques such as oblivious transfer. Extensive experiments on popular models, including ResNet50 and DenseNet121, show that Skybolt achieves a $5.4-7.3 \times $ reduction in inference latency, accompanied by a $20.1-39.6 \times $ decrease in communication cost compared to HELiKs.