task_categories:text-classification task_categories:summarization size_categories:100M<n<1T language:en license:apache-2.0 region:us
---
license: apache-2.0
task_categories:
- text-classification
- summarization
language:
- en
pretty_name: Pseudonymization data
size_categories:
- 100M<n<1T
---
This repository contains all the datasets used in our paper "Privacy- and Utility-Preserving NLP with Anonymized data: A case study of Pseudonymization" (https://aclanthology.org/2023.trustnlp-1.20).

# Dataset Card for Pseudonymization data

## Dataset Description

- **Homepage:** https://huggingface.co/datasets/grammarly/pseudonymization-data
- **Paper:** https://aclanthology.org/2023.trustnlp-1.20/
- **Point of Contact:** oleksandr.yermilov@ucu.edu.ua

### Dataset Summary

This dataset repository contains all the datasets, used in our paper. It includes datasets for different NLP tasks, pseudonymized by different algorithms; a dataset for training Seq2Seq model which translates text from original to "pseudonymized"; and a dataset for training model which would detect if the text was pseudonymized. 


### Languages
English.

## Dataset Structure
Each folder contains preprocessed train versions of different datasets (e.g, in the `cnn_dm` folder there will be preprocessed CNN/Daily Mail dataset). Each file has a name, which corresponds with the algorithm from the paper used for its preprocessing (e.g. `ner_ps_spacy_imdb.csv` is imdb dataset, preprocessed with NER-based pseudonymization using FLAIR system). 
I

## Dataset Creation
Datasets in `imdb` and `cnn_dm` folders were created by pseudonymizing corresponding datasets with different pseudonymization algorithms. 

Datasets in `detection` folder are combined original datasets and pseudonymized datasets, grouped by pseudonymization algorithm used.

Datasets in `seq2seq` folder are datasets for training Seq2Seq transformer-based pseudonymization model. At first, a dataset was fetched from Wikipedia articles, which was preprocessed with either NER-PS<sub>FLAIR</sub> or NER-PS<sub>spaCy</sub> algorithms.

### Personal and Sensitive Information

This datasets bring no sensitive or personal information; it is completely based on data present in open sources (Wikipedia, standard datasets for NLP tasks).

## Considerations for Using the Data

### Known Limitations

Only English texts are present in the datasets. Only a limited part of named entity types are replaced in the datasets. Please, also check the Limitations section of our paper.

## Additional Information

### Dataset Curators
Oleksandr Yermilov (oleksandr.yermilov@ucu.edu.ua)

### Citation Information

```
@inproceedings{yermilov-etal-2023-privacy,
    title = "Privacy- and Utility-Preserving {NLP} with Anonymized data: A case study of Pseudonymization",
    author = "Yermilov, Oleksandr  and
      Raheja, Vipul  and
      Chernodub, Artem",
    booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.trustnlp-1.20",
    doi = "10.18653/v1/2023.trustnlp-1.20",
    pages = "232--241",
    abstract = "This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques better to balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available.",
}
```