Anonymizing free-text data is harder than it seems. While structured databases have well-established anonymization techniques, textual data — like invoices, resumes, or medical records — poses unique challenges. Personally identifiable information (PII) can appear anywhere, in unpredictable formats, and how to modify it while preserving the dataset's usefulness?

Let's explore a practical, open-source 2-step approach to text anonymization: (1) detecting PII using NER models and (2) replacing it while preserving key dataset characteristics (e.g. document formatting, statistical distributions). We will demonstrate how to build a robust pipeline leveraging tools such as pre-trained PII detection models, gliner for fine-tuning, or Faker for generating meaningful replacements.

Ideal for those with a basic understanding of NLP, this session offers practical insights for anyone working with sensitive textual data.