Autor: George Curta ·
Methodology
anonym.legal makes specific claims about detecting and anonymizing personal data. This page explains how the system actually works, how we arrive at the numbers we publish, and — just as importantly — where the method has limits, so you can judge it against your own compliance standard.
How detection works
Detection combines machine-learning named-entity recognition with deterministic pattern matching and contextual validation. The model proposes spans of likely personal data; pattern rules confirm structured identifiers (emails, card numbers, national IDs) by format and checksum where one exists; and surrounding context is used to resolve ambiguous mentions. A span is only redacted when these signals agree, which keeps over-redaction of ordinary text low without relying on a single fragile heuristic.
Entity coverage and how we count it
We publish coverage of 273 entity types. That figure counts the distinct categories the system is configured to recognize — names, contact details, government identifiers, financial data, health data, and domain-specific identifiers — not the number of patterns or model labels behind them. Coverage varies by entity: well-structured identifiers are detected more reliably than free-form references, and you can narrow or extend the active set per job using presets.
Language support
Detection runs across 48 languages. Accuracy is highest for languages with large training corpora and regular orthography, and lower for low-resource languages, transliterated text, and mixed-language documents. We do not claim identical performance across every language, and we recommend reviewing output on a representative sample before processing a new language at volume.
Reversible vs. irreversible anonymization
You choose the transformation. Irreversible redaction removes the original value and cannot be undone. Reversible tokenization replaces values with consistent tokens that an authorized holder of the key can later restore — useful when an auditor or downstream system must verify figures against source records, then have access revoked. Reversible mode is pseudonymization under the GDPR, not anonymization; treat tokenized output as still personal data while the mapping exists.
Accuracy, false positives, and false negatives
No automated system detects personal data perfectly. False negatives (missed PII) and false positives (redacting non-PII) both occur, and their rates depend on document type, language, formatting, and OCR quality on scanned material. We tune toward recall on sensitive categories, but residual risk remains. anonym.legal is a control that reduces exposure — it is not a guarantee of complete de-identification.
Human verification and your responsibility
Because detection is probabilistic, a human should review output before sensitive material is released, especially for legal, healthcare, and regulatory use. You remain the controller of your data and are responsible for validating that anonymization meets your legal standard — for example HIPAA Expert Determination or a GDPR anonymization assessment. We document limitations openly so you can build the right review step around the tool rather than assume it away.
Questions about the method
Want detail on how a specific entity type or language is handled, or how a published number is derived? Ask — we would rather explain than overstate.