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

TL;DR

This paper reviews recent advances and challenges in private information retrieval, distributed computing, and federated learning, emphasizing privacy-preserving techniques and their interconnections across these domains.

Contribution

It provides a comprehensive overview of recent progress, key techniques, and open problems in privacy-preserving methods for retrieval, computing, and learning.

Findings

Breakthrough results in private information retrieval

Advances in privacy-preserving distributed computing

Progress in federated learning with privacy guarantees

Abstract

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.