Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an increasing need to protect personal, financial and healthcare information. Conventional centralized data processing methods expose sensitive data to risk of breaches, compelling the need to use decentralized and secure data methods. This paper gives a detailed review of privacy-saving mechanisms in the cloud platform, such as statistical approaches like differential privacy and cryptographic solutions like homomorphic encryption. Federated analytics and federated learning, two distributed learning frameworks, are also discussed. Their principles, applications, benefits, and limitations are reviewed, with roles of use in the fields of healthcare, finance, IoT, and industrial cases. Comparative analyses measure trade-offs in security, efficiency, scalability, and accuracy, and investigations are done of emerging hybrid frameworks to provide better privacy protection. Critical issues, including computational overhead, privacy-utility trade-offs, standardization, adversarial threats, and cloud integration are also addressed. This review examines in detail the recent privacy-protecting approaches in cloud computation and offers scholars and practitioners crucial information on secure and effective solutions to data processing.

翻译：隐私保护数据处理指在保证机密性的前提下对敏感数据进行计算与分析的方法与模型。随着云计算及依赖数据的应用持续扩展，对个人、金融及医疗信息的保护需求日益增长。传统的集中式数据处理方法使敏感数据面临泄露风险，因此迫切需要采用去中心化且安全的数据处理方法。本文详细综述了云平台中的隐私保护机制，包括差分隐私等统计方法以及同态加密等密码学解决方案，同时探讨了联邦分析与联邦学习这两种分布式学习框架。文章系统回顾了其基本原理、应用场景、优势与局限，并阐述了它们在医疗健康、金融、物联网及工业案例中的具体作用。通过对比分析，本文评估了各类方法在安全性、效率、可扩展性与准确性之间的权衡，并对新兴的混合框架进行了研究，以探索更优的隐私保护方案。文中还讨论了关键挑战，包括计算开销、隐私与效用的权衡、标准化问题、对抗性威胁以及云平台集成等。本综述深入剖析了云计算中隐私保护技术的最新进展，为学者与实践者提供了关于安全高效数据处理解决方案的重要参考。