Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced, and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain.

中文翻译：

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医学和生物信息学中机器学习的同态加密

机器学习和统计技术是分析大量医学和基因组数据的强大工具。另一方面，道德问题和隐私法规阻止了这些数据的免费共享。诸如完全同态加密 (FHE) 之类的加密技术可以对加密数据进行评估。使用 FHE，机器学习模型（如深度学习、决策树和朴素贝叶斯）已被实施用于使用医疗数据的隐私保护应用程序。这些应用包括对加密数据进行分类和在加密数据上训练模型。FHE 还被证明可以实现安全的基因组算法，例如亲子鉴定和血统测试以及全基因组关联研究的隐私保护应用。该调查概述了完全同态加密及其在医学和生物信息学中的应用。介绍了 FHE 背后的高级概念及其历史，并提供了当前开源实现的详细信息。回顾了机器学习和生物信息学中隐私保护技术的全同态加密状态，以及如何在加密域中实施这些方法的描述。