In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.

中文翻译：

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在同态加密医学数据上应用深度神经网络。

近年来，机器学习在广泛的领域中取得了最先进的成就，受到了医疗界的极大关注。尽管他们有能力在个性化医学中提供解决方案，但是在许多情况下，对患者健康信息的机密性的严格规定阻碍了在临床工作流程中采用基于深度学习的解决方案。为了在不泄露基础数据的情况下处理敏感的健康信息，我们提出了一种基于完全同态加密（FHE）的解决方案。所考虑的加密方案MORE（用于随机化或加密的矩阵运算）使神经网络模型内的计算能够以相对较小的计算开销直接在浮点数据上执行。我们考虑了众所周知的MNIST数字识别问题，以评估该方法的可行性，并表明在将深度学习应用于更多同态数据时，性能不会降低。为了进一步评估该方法对医疗保健应用的适用性，我们首先在加密数据上训练模型，以估计全身循环（WBC）血液动力学模型的输出，然后提供对加密的X射线冠状动脉造影医学图像进行分类的解决方案。研究结果突显了所提议的保护隐私的深度学习方法通​​过在合理的时间内提供与未加密模型等效的结果而胜过现有方法的潜力。最后，