本研究的目的是探索机器学习和全同态加密（FHE）在私人病理评估中的适用性，重点是用于机密医疗数据分类的支持向量机（SVM）的推理阶段。引入了利用 Cheon-Kim-Kim-Song (CKKS) FHE 方案，促进在加密数据集上执行 SVM 推理。该框架确保了患者数据的隐私，并消除了分析过程中解密的必要性。此外，还提出了一种有效的特征提取技术，用于将医学图像转换为矢量表示。该系统在各种数据集上的评估证实了其实用性和有效性。所提出的方法可提供与传统非加密 SVM 推理相当的分类准确性和性能，同时针对针对 CKKS 方案的既定加密攻击保持 128 位安全级别。安全推理过程在短短几秒钟的时间内执行。这项研究的结果强调了 FHE 在提高生物信息学分析的安全性和效率方面的可行性，可能使心脏病学、肿瘤学和医学影像等领域受益。这项研究对于保护隐私的机器学习的未来具有重要意义，促进诊断程序、定制医疗和临床研究的进步。© 2024。作者。

The objective of this research is to explore the applicability of machine learning and fully homomorphic encryption (FHE) in the private pathological assessment, with a focus on the inference phase of support vector machines (SVM) for the classification of confidential medical data.A framework is introduced that utilizes the Cheon-Kim-Kim-Song (CKKS) FHE scheme, facilitating the execution of SVM inference on encrypted datasets. This framework ensures the privacy of patient data and negates the necessity of decryption during the analytical process. Additionally, an efficient feature extraction technique is presented for the transformation of medical imagery into vectorial representations.The system's evaluation across various datasets substantiates its practicality and efficacy. The proposed method delivers classification accuracy and performance on par with traditional, non-encrypted SVM inference, while upholding a 128-bit security level against established cryptographic attacks targeting the CKKS scheme. The secure inference process is executed within a temporal span of mere seconds.The findings of this study underscore the viability of FHE in enhancing the security and efficiency of bioinformatics analyses, potentially benefiting fields such as cardiology, oncology, and medical imagery. The implications of this research are significant for the future of privacy-preserving machine learning, promoting progress in diagnostic procedures, tailored medical treatments, and clinical investigations.© 2024. The Author(s).