期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 40, 期 3, 页码 729-748出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2022.3142358
关键词
Databases; Servers; Privacy; Data privacy; Information retrieval; Distributed databases; Costs; Private information retrieval; private distributed computing; private distributed learning; federated learning
资金
- Army Research Office (ARO) [W911NF2010142]
- National Science Foundation (NSF) [CCF 1713977, ECCS 18-07348, CNS 17-15947, CAREER 16-51492, CCF 21-00013, CCF-2007067]
- U.S. Department of Defense (DOD) [W911NF2010142] Funding Source: U.S. Department of Defense (DOD)
This article discusses privacy protection in cyberspace, focusing on information retrieval, distributed computing, and distributed learning. It presents problems, solutions, and breakthroughs in each field. The article also explores the interconnections between these topics and raises some open questions.
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.
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