Expectation maximization (EM) is a clustering-based machine learning algorithm that is widely used in many areas of science (e.g., bioinformatics and computer vision) to find maximum likelihood and maximum a posteriori estimates for models with latent variables. To deploy such an algorithm in cloud environments, security and privacy issues need be considered to avoid data breaches or abuses by external malicious parties or even by cloud service providers. However, the processing performance of the EM algorithm poses a challenge in terms of building a secure environment. This article describes an innovative and practical privacy-preserving EM algorithm for cloud systems that addresses this challenge, and estimates the EM parameters in an accurate and secure manner. Fully homomorphic encryption (FHE) is used to ensure the privacy of both the EM algorithm computations and the users’ sensitive data in the cloud. A distributed-based approach is also proposed to overcome the overheads of FHE computations and ensure a fast convergence of the EM algorithm. The conducted experiments demonstrate a significant improvement in the convergence time of the distributed EM algorithm, while achieving a high level of accuracy and reducing the associated computational FHE overheads.

中文翻译：

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云计算中高斯混合建模的分布式隐私保护期望最大化

期望最大化 (EM) 是一种基于聚类的机器学习算法，广泛用于许多科学领域（例如，生物信息学和计算机视觉），以寻找具有潜在变量的模型的最大似然和最大后验估计。为了在云环境中部署这样的算法，需要考虑安全和隐私问题，以避免外部恶意方甚至云服务提供商的数据泄露或滥用。然而，EM算法的处理性能在构建安全环境方面提出了挑战。本文介绍了一种用于云系统的创新且实用的隐私保护 EM 算法，该算法解决了这一挑战，并以准确和安全的方式估计了 EM 参数。全同态加密 (FHE) 用于确保 EM 算法计算和用户在云中的敏感数据的隐私。还提出了一种基于分布式的方法来克服 FHE 计算的开销并确保 EM 算法的快速收敛。进行的实验表明分布式 EM 算法的收敛时间有显着改善，同时实现了高水平的准确性并减少了相关的计算 FHE 开销。