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

arXiv: https://arxiv.org/abs/2108.00026 · HTML: https://mlsanity.com/paper/2108.00026/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2021.

## Built on by (most cited first, 12 of 12)

- [Differentially Private Federated Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2203.17028/) (2022)
- [Private Counterfactual Retrieval](https://mlsanity.com/paper/2410.13812/) (2024)
- [Privacy-aware Berrut Approximated Coded Computing for Federated Learning](https://mlsanity.com/paper/2405.01704/) (2024)
- [Collaborative Inference over Wireless Channels with Feature Differential Privacy](https://mlsanity.com/paper/2410.19917/) (2024)
- [Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning](https://mlsanity.com/paper/2505.06759/) (2025)
- [Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale](https://mlsanity.com/paper/2608.25735/) (2026)
- [Private Federated Submodel Learning with Sparsification](https://mlsanity.com/paper/2205.15992/) (2022)
- [Secure Embedding Aggregation for Federated Representation Learning](https://mlsanity.com/paper/2206.09097/) (2022)
- [Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load](https://mlsanity.com/paper/2304.08589/) (2023)
- [Private Counterfactual Retrieval With Immutable Features](https://mlsanity.com/paper/2411.10429/) (2024)
- [Federated One-Shot Learning with Data Privacy and Objective-Hiding](https://mlsanity.com/paper/2504.21182/) (2025)
- [What If, But Privately: Private Counterfactual Retrieval](https://mlsanity.com/paper/2508.03681/) (2025)

## Abstract (arXiv)

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.