Efficient Secure Inference Scheme in Multiparty Settings for Industrial Internet of Things
Jie Lin, Yinbin Miao, Linfeng Wei, Tao Leng, Kim‐Kwang Raymond Choo · IEEE Transactions on Industrial Informatics · 2024
Secure inference is the main technology to avoid privacy leakage when reasoning with machine learning models in multiparty settings for industrial Internet of Things (IoT). Existing solutions based on secure multiparty computation have been extensively explored in both academic and industrial fields, but these solutions are deployed in single user single model provider setting. When multiparty collaborate to handle tasks in multiuser multimodel provider setting, these solutions are no longer applicable as the mechanism of the linear computation protocol restricting the number of parties increases. In addition, these existing schemes need to conduct multiple checks during the inference of malicious models, which results in additional communication overhead. To solve these issues, we propose a multiparty secure inference scheme by using an improved multiplication protocol for achieving matrix multiplication, which achieves efficient linear calculation and supports adaptive parties. We also design a new check protocol to inspect calculation results of all layers with just one execution, which achieves lower communication and calculation overheads. Formal security analysis proves that our scheme achieves malicious security in the honest-majority setting, and extensive experiments demonstrate that our scheme reduces the communication costs by about 30% in large neural networks when compare with previous solutions.