Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges
Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges
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DOI：
10.1109/jsac.2022.3142358
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发表时间：
2021-07
影响因子：
16.4
通讯作者：
S. Ulukus;S. Avestimehr;M. Gastpar;S. Jafar;R. Tandon;Chao Tian
中科院分区：
文献类型：
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作者：
S. Ulukus;S. Avestimehr;M. Gastpar;S. Jafar;R. Tandon;Chao Tian
我们的大部分生活都在网络空间中进行。人类的隐私概念转化为网络空间中发生的许多功能的隐私概念。本文重点讨论三个功能：如何从网络空间中私下检索信息（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），以及如何从分布在多个用户中的私有数据中学习/训练机器学习模型（分布式（联邦）学习中的隐私）。文章激励每个隐私设置，描述了问题的制定，总结了突破性的成果，在历史上的每个问题，并给出了最近的结果，并讨论了一些主要的想法，出现在每个领域。此外，还沿着讨论了这三个主题之间的交叉技术和相互联系，以及一系列悬而未决的问题和挑战。
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.