Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

doi:10.1109/JSAC.2022.3142358

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

2022-03-01

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DOI：10.1109/JSAC.2022.3142358

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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.

Keywords:

Databases

Servers

Privacy

Data privacy

Information retrieval

Distributed databases

Costs

Private information retrieval

private distributed computing

private distributed learning

federated learning

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

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