Deep learning (DL) has made significant advancements in tomographic imaging, particularly in low-dose computed tomography (LDCT) denoising. A recent trend involves servers training powerful models with large amounts of self-collected private data and providing application programming interfaces (APIs) for users, such as Chat-GPT. To avoid model leakage, users are required to upload their data to the server model, but this way raises public concerns about the potential risk of privacy disclosure, especially for medical data. Hence, to alleviate related concerns, in this paper, we propose to directly denoise LDCT in the encrypted domain to achieve privacy-preserving cloud services without exposing private data to the server. To this end, we employ homomorphic encryption to encrypt private LDCT data, which is then transferred to the server model trained with plaintext LDCT for further denoising. However, since traditional operations, such as convolution and linear transformation, in DL methods cannot be directly used in the encrypted domain, we transform the fundamental mathematic operations in the plaintext domain into the operations in the encrypted domain. In addition, we present two interactive frameworks for linear and nonlinear models in this paper, both of which can achieve lossless operating. In this way, the proposed methods can achieve two merits, the data privacy is well protected and the server model is free from the risk of model leakage. Moreover, we provide theoretical proof to validate the lossless property of our framework. Finally, experiments were conducted to demonstrate that the transferred contents are well protected and cannot be reconstructed. The code will be released once the paper is accepted.

翻译：深度学习在断层成像领域取得了显著进展，特别是在低剂量计算机断层扫描（LDCT）去噪方面。近期趋势涉及服务器利用大量自收集私有数据训练强大模型，并向用户提供应用程序编程接口（API），例如Chat-GPT。为避免模型泄露，用户需将数据上传至服务器模型，但这种方式引发了公众对隐私泄露潜在风险的担忧，尤其涉及医疗数据。因此，为缓解相关担忧，本文提出直接在加密域中对低剂量CT图像进行去噪，以实现隐私保护的云服务，而无需向服务器暴露私有数据。为此，我们采用同态加密技术对私有低剂量CT数据进行加密，并将其传输至使用明文低剂量CT数据训练的服务器模型进行进一步去噪。由于深度学习中的传统运算（如卷积和线性变换）无法直接在加密域中使用，本文将明文域中的基础数学运算转换为加密域中的运算。此外，本文提出了针对线性模型和非线性模型的两种交互框架，两者均可实现无损运算。通过这种方式，所提方法具有两大优点：数据隐私得到良好保护，且服务器模型可避免模型泄露风险。同时，我们提供理论证明以验证框架的无损特性。最后，实验表明传输内容得到充分保护且无法被重建。代码将在论文被接收后公开。