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

Sennur Ulukus, Salman Avestimehr, Michael Gastpar, Syed A. Jafar, Ravi Tandon, Chao Tian

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)729-748
Number of pages20
JournalIEEE Journal on Selected Areas in Communications
Issue number3
StatePublished - Mar 1 2022


  • Private information retrieval
  • federated learning
  • private distributed computing
  • private distributed learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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