Microsoft Presidio Stable Numbers: Deterministic Masking for Structured Numeric Data

Microsoft Presidio Stable Numbers is the latest update to Microsoft’s open-source data protection library, designed to detect and anonymize structured numerical identifiers with consistent accuracy. This release adds deterministic masking for data like credit card numbers, phone numbers, IBANs, and other stable numeric formats. Stable Numbers ensures that the same detected value is replaced with the same anonymized token every time, enabling analytics and testing workflows without risking sensitive information.

Built on Microsoft Presidio's proven PII detection pipeline, Stable Numbers uses regex patterns and context-based rules to identify numeric sequences. It supports configurable anonymizers, so masked data can follow precise formats. The stability property means referential integrity is maintained across datasets and logs, making it easier to trace entities without exposing real identifiers.

For engineers managing pipelines at scale, this feature reduces false positives and enables safe use of production-like datasets in lower environments. Integration is straightforward: extend your Presidio configuration, set your anonymizer to "hash" or another stable function, and deploy. The library is written in Python, runs locally or in containerized environments, and integrates into existing ETL jobs or API services.

Microsoft Presidio Stable Numbers addresses real compliance needs under GDPR, PCI DSS, HIPAA, and other data protection standards. The deterministic approach solves a long-standing trade-off between privacy and data usability, making it ideal for QA, AI training, and cross-system debugging without leaking personal data.

Run it in your staging data flow. Watch identifiers become unrecognizable, yet remain linkable for safe analysis.

See Microsoft Presidio Stable Numbers live in minutes with hoop.dev — build, test, and secure your pipelines without slowing down.