隐私保护的加密低剂量CT去噪
Privacy-Preserving Encrypted Low-Dose CT Denoising
AI总结：
本文提出在加密域中使用同态加密进行低剂量CT去噪，通过将明文数学运算转换为加密域操作并设计线性与非线性交互框架，实现隐私保护且无损的云服务，避免数据泄露与模型泄露风险。
摘要：
深度学习（DL）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。近期的一个趋势是服务器利用大量自收集的私有数据训练强大的模型，并为用户提供应用程序编程接口（API），例如Chat-GPT。为避免模型泄露，用户需要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，尤其是对于医疗数据。因此，为缓解相关担忧，本文提出直接在加密域中对LDCT进行去噪，以实现隐私保护的云服务，而无需将私有数据暴露给服务器。为此，我们采用同态加密对私有LDCT数据进行加密，然后将加密数据传输至使用明文LDCT训练的服务器模型进行进一步去噪。然而，由于DL方法中的传统操作（如卷积和线性变换）无法直接在加密域中使用，我们将明文域中的基础数学运算转换为加密域中的运算。此外，本文提出了两种分别用于线性模型和非线性模型的交互式框架，两者均可实现无损操作。通过这种方式，所提出的方法能够实现两大优点：数据隐私得到良好保护，且服务器模型免于模型泄露的风险。此外，我们提供了理论证明以验证我们框架的无损特性。最后，实验表明传输内容得到了良好保护且无法被重建。代码将在论文被接收后发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
