Light weighted privacy protection ViT inference framework based on secret sharing - MA Min; FU Yu; HUANG Kai; JIA Xiaofeng (Tongxin xuebao)
The ViT (vision transformer) inference framework, which was widely used in image processing, was found to have a risk of leaking user privacy data. However, existing privacy protection inference frameworks had problems such as low computational efficiency and high online communication volume. To address this issue, a highly efficient privacy protection inference framework SViT was proposed. Two edge servers collaborated to execute secure computing protocols based on secret sharing design, such as SSoftmax, SLayerNorm, SGeLU, etc. While maintaining the original framework structure of ViT-B/16, the problem of large inference overhead in privacy protection framework was solved. Theoretical analysis and experiments show that compared to Crypton, SViT has improved computational efficiency by 2~6 times and online communication overhead by 4~14 times, respectively.