# Privacy in Open Search: A Review of Challenges and Solutions

arXiv: https://arxiv.org/abs/2110.10720 · HTML: https://mlsanity.com/paper/2110.10720/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [Federated Learning of N-gram Language Models](https://mlsanity.com/paper/1910.03432/) (2019)
- [Tagvisor: A Privacy Advisor for Sharing Hashtags](https://mlsanity.com/paper/1802.04122/) (2018)
- [Facilitating SQL Query Composition and Analysis](https://mlsanity.com/paper/2002.09091/) (2020)
- [Computing Local Sensitivities of Counting Queries with Joins](https://mlsanity.com/paper/2004.04656/) (2020)

## Built on by (most cited first, 3 of 3)

- [How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing](https://mlsanity.com/paper/2205.10095/) (2022)
- [Unlearning for Federated Online Learning to Rank: A Reproducibility Study](https://mlsanity.com/paper/2505.12791/) (2025)
- [A Large-Scale Web Search Dataset for Federated Online Learning to Rank](https://mlsanity.com/paper/2508.12353/) (2025)

## Abstract (arXiv)

Privacy is of worldwide concern regarding activities and processes that include sensitive data. For this reason, many countries and territories have been recently approving regulations controlling the extent to which organizations may exploit data provided by people. Artificial intelligence areas, such as machine learning and natural language processing, have already successfully employed privacy-preserving mechanisms in order to safeguard data privacy in a vast number of applications. Information retrieval (IR) is likewise prone to privacy threats, such as attacks and unintended disclosures of documents and search history, which may cripple the security of users and be penalized by data protection laws. This work aims at highlighting and discussing open challenges for privacy in the recent literature of IR, focusing on tasks featuring user-generated text data. Our contribution is threefold: firstly, we present an overview of privacy threats to IR tasks; secondly, we discuss applicable privacy-preserving mechanisms which may be employed in solutions to restrain privacy hazards; finally, we bring insights on the tradeoffs between privacy preservation and utility performance for IR tasks.