# Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting

arXiv: https://arxiv.org/abs/2303.03053 · HTML: https://mlsanity.com/paper/2303.03053/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [ER-AE: Differentially Private Text Generation for Authorship Anonymization](https://mlsanity.com/paper/1907.08736/) (2019)
- [ADePT: Auto-encoder based Differentially Private Text Transformation](https://mlsanity.com/paper/2102.01502/) (2021)
- [Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition](https://mlsanity.com/paper/1910.10872/) (2019)
- [DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting](https://mlsanity.com/paper/2208.10400/) (2022)
- [FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning](https://mlsanity.com/paper/2003.09288/) (2020)
- [Assessing Demographic Bias in Named Entity Recognition](https://mlsanity.com/paper/2008.03415/) (2020)

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

- [Reducing Privacy Risks in Online Self-Disclosures with Language Models](https://mlsanity.com/paper/2311.09538/) (2023)
- [The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques](https://mlsanity.com/paper/2603.08207/) (2026)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)

## Abstract (arXiv)

Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included. Textual data often contains sensitive information like person names or locations. In this work, we investigate how removing personally identifiable information (PII) as well as applying differential privacy (DP) rewriting can enable text with privacy-relevant information to be used for crowdsourcing. We find that DP-rewriting before crowdsourcing can preserve privacy while still leading to good label quality for certain tasks and data. PII-removal led to good label quality in all examined tasks, however, there are no privacy guarantees given.