Title: Denoising Differential Privacy in Split Learning. Abstract: Differential Privacy (DP) is applied in split learning to address privacy concerns about data leakage. Previous work combines split neural network (SplitNN) training with DP by adding noise to the intermediate results during the forward pass. Unfortunately, DP noise injection significantly degrades the training accuracy of SplitNN. This paper focuses on improving the training accuracy of DP-protected SplitNNs without sacrificing the privacy guarantee. We propose two denoising techniques, namely scaling and random masking. Our theoretical investigation shows that both of our techniques achieve accurate estimation of the intermediate variables during the forward pass of SplitNN training. Our experiments with real networks demonstrate that our denoising approach allows SplitNN training that can tolerate high levels of DP noise while achieving almost the same accuracy as the non-private (i.e., non-DP protected) baseline. Interestingly, we show that after applying our techniques, the resultant network is more resilient against a state-of-the-art attack, compared to the plain DP-protected baseline.