This tutorial offers a hands-on introduction to Microsoft Presidio, an open-source framework for detecting and anonymizing personally identifiable information (PII) in free-form text. It walks through the installation and setup of Presidio’s analyzer and anonymizer components, leveraging spaCy for NLP tasks. Readers learn how to detect common PII entities like names and phone numbers, as well as define custom recognizers for specialized use cases such as academic titles or government-issued IDs (e.g., PAN, Aadhaar).
In addition to detection, the guide dives into various anonymization strategies, including simple replacements and a custom SHA-256-based operator that supports consistent re-anonymization across datasets. This is particularly useful for maintaining linkability of pseudonymized entities while preserving privacy. Overall, the tutorial equips developers and data practitioners with practical tools to integrate PII protection workflows into real-time applications, highlighting both modularity and extensibility of the Presidio framework.
Full Tutorial: <a href="https://www.marktechpost.com/2025/06/24/getting-started-with-microsofts-presidio-a-step-by-step-guide-to-detecting-and-anonymizing-personally-identifiable-information-pii-in-text/" target="_blank" rel="noopener nofollow">https://www.marktechpost.com/2025/06/24/getting-started-with-microsofts-presidio-a-step-by-step-guide-to-detecting-and-anonymizing-personally-identifiable-information-pii-in-text/</a>
Codes: <a href="https://github.com/Marktechpost/AI-Notebooks/blob/main/Presidio.ipynb" target="_blank" rel="noopener nofollow">https://github.com/Marktechpost/AI-Notebooks/blob/main/Presidio.ipynb</a>

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A Practical Guide to PII Detection and Anonymization Using Microsoft Presidio