Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve information from cyberspace (privacy in information retrieval), how to privately leverage large-scale distributed/parallel processing (privacy in distributed computing), and how to learn/train machine learning models from private data spread across multiple users (privacy in distributed (federated) learning). The article motivates each privacy setting, describes the problem formulation, summarizes breakthrough results in the history of each problem, and gives recent results and discusses some of the major ideas that emerged in each field. In addition, the cross-cutting techniques and interconnections between the three topics are discussed along with a set of open problems and challenges.

中文翻译：

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私人检索、计算和学习：最近的进展和未来的挑战

我们的大部分生活都是在网络空间中进行的。人类的隐私概念转化为网络空间中发生的许多功能的网络隐私概念。本文重点介绍三个这样的功能：如何从网络空间私下检索信息（信息检索中的隐私），如何私下利用大规模分布式/并行处理（分布式计算中的隐私），以及如何从网络中学习/训练机器学习模型。私有数据分布在多个用户之间（分布式（联合）学习中的隐私）。文章激发了每个隐私设置，描述了问题的表述，总结了每个问题历史上的突破性成果，并给出了最近的结果并讨论了每个领域出现的一些主要思想。此外，