The past decade has seen a rapid adoption of Artificial Intelligence (AI),
specifically the deep learning networks, in Internet of Medical Things (IoMT)
ecosystem. However, it has been shown recently that the deep learning networks
can be exploited by adversarial attacks that not only make IoMT vulnerable to
the data theft but also to the manipulation of medical diagnosis. The existing
studies consider adding noise to the raw IoMT data or model parameters which
not only reduces the overall performance concerning medical inferences but also
is ineffective to the likes of deep leakage from gradients method. In this
work, we propose proximal gradient split learning (PSGL) method for defense
against the model inversion attacks. The proposed method intentionally attacks
the IoMT data when undergoing the deep neural network training process at
client side. We propose the use of proximal gradient method to recover gradient
maps and a decision-level fusion strategy to improve the recognition
performance. Extensive analysis show that the PGSL not only provides effective
defense mechanism against the model inversion attacks but also helps in
improving the recognition performance on publicly available datasets. We report
14.0$\%$, 17.9$\%$, and 36.9$\%$ gains in accuracy over reconstructed and
adversarial attacked images, respectively.

本研究提出了一种名为 PSGL 的方法，通过攻击廓清晰度和深度神经网络的训练过程，并引入贴近梯度方法来防御模型反演攻击，并在公开数据集上取得 14.0％、17.9％和 36.9％的诊断准确率提升。