Current State in Privacy-Preserving Text Preprocessing for Domain-Agnostic NLP

TL;DR

This paper reviews current privacy-preserving text preprocessing methods for domain-agnostic NLP, emphasizing the importance of anonymization techniques to protect sensitive information in large language models.

Contribution

It provides an overview of existing anonymization approaches for textual data in NLP, highlighting their relevance across various domains.

Findings

Various pre-processing techniques can mask private information

Anonymization methods vary in effectiveness and domain applicability

Protecting privacy remains a critical challenge in NLP

Abstract

Privacy is a fundamental human right. Data privacy is protected by different regulations, such as GDPR. However, modern large language models require a huge amount of data to learn linguistic variations, and the data often contains private information. Research has shown that it is possible to extract private information from such language models. Thus, anonymizing such private and sensitive information is of utmost importance. While complete anonymization may not be possible, a number of different pre-processing approaches exist for masking or pseudonymizing private information in textual data. This report focuses on a few of such approaches for domain-agnostic NLP tasks.