Registry / auth-security / presidio-anonymizer

presidio-anonymizer

library2.2.364pypypi✓ verified 26d ago

Presidio Anonymizer is a Python-based module designed for anonymizing detected Personally Identifiable Information (PII) entities in text. It offers a range of built-in operators (e.g., replace, mask, redact, hash, encrypt) and supports custom anonymization logic. It also includes deanonymization capabilities for reversible operations like decryption. The library is actively maintained by Microsoft, with frequent releases, and is currently at version 2.2.362.

auth-security data

pip install presidio-anonymizer

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · PRESIDIO-ANONYMIZE

presidio-anonymizer

auth-securitypythonv2.2.364

Install

13.0s avg

Import

109ms

Disk

32MB

Pass rate

8/ 10

Env Coverage8 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.2.364 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✓ —

✓ 14.3s

py 3.11

✓ —

✓ 13.2s

py 3.12

✓ —

✓ 11.85s

py 3.13

✓ —

✓ 12.7s

py 3.9

1/2 runs

32MB installed

● package 32MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

AnonymizerEngine

✓ from presidio_anonymizer import AnonymizerEngine

RecognizerResult

✓ from presidio_anonymizer.entities import RecognizerResult

✗ from presidio_analyzer.recognizer_result import RecognizerResult

While presidio-analyzer also has a RecognizerResult, it's a separate type. For direct use with AnonymizerEngine, import from `presidio_anonymizer.entities` to avoid mypy type errors.

OperatorConfig

✓ from presidio_anonymizer.entities import OperatorConfig

This quickstart demonstrates how to use `AnonymizerEngine` to anonymize text. It manually defines `RecognizerResult` objects, which would typically be generated by `presidio-analyzer`. It shows custom operators for different entity types.

from presidio_anonymizer import AnonymizerEngine from presidio_anonymizer.entities import RecognizerResult, OperatorConfig # Sample text and mock analyzer results (typically from presidio-analyzer) text = "My name is John Doe and my phone number is 123-456-7890." analyzer_results = [ RecognizerResult(entity_type="PERSON", start=11, end=19, score=0.9), RecognizerResult(entity_type="PHONE_NUMBER", start=38, end=50, score=0.8), ] # Initialize the anonymizer engine anonymizer = AnonymizerEngine() # Define anonymization operators # Here, PERSON will be replaced with "<PERSON>", and PHONE_NUMBER will be masked operators = { "PERSON": OperatorConfig("replace", {"new_value": "<PERSON>"}), "PHONE_NUMBER": OperatorConfig("mask", { "masking_char": "*", "chars_to_mask": 10, "from_end": True }) } # Perform anonymization anonymized_result = anonymizer.anonymize( text=text, analyzer_results=analyzer_results, operators=operators ) print(f"Original text: {text}") print(f"Anonymized text: {anonymized_result.text}")

Debug

Known issues

breakingThe default behavior of the 'hash' operator changed in version 2.2.361. It now uses a random salt by default for enhanced security, which means the same PII value will yield different hashes across calls or entities. This breaks referential integrity unless a salt is explicitly provided.

fix

If referential integrity (same hash for the same value) is required, you must explicitly provide a consistent 'salt' parameter to the 'hash' operator config. Use a secure method to generate and store this salt.

affects: >=2.2.361

gotchaWhen using `presidio-analyzer` and `presidio-anonymizer` together in a typed Python environment, `mypy` might report type errors due to `RecognizerResult` existing in both packages with incompatible types. The `AnonymizerEngine` expects `RecognizerResult` from `presidio_anonymizer.entities`.

fix

Always import `RecognizerResult` specifically from `presidio_anonymizer.entities` when passing results to `AnonymizerEngine`. You may need to cast or convert `presidio-analyzer`'s `RecognizerResult` objects if you generate them from `AnalyzerEngine` and need to satisfy strict type checking.

affects: >=2.2.354

gotchaStarting from version 2.2.359, many country-specific recognizers (e.g., SgFinRecognizer, AuAbnRecognizer) are disabled by default to prevent false positives when they are not explicitly needed. Users expecting these to work out-of-the-box might find them inactive.

fix

If specific country-specific recognizers are required, ensure they are explicitly enabled or configured within your `presidio-analyzer` setup.

affects: >=2.2.359

gotchaIn versions prior to 2.2.362, `AnonymizerEngine` could fail to anonymize all instances of an entity if multiple identical entities were separated only by spaces, potentially leading to PII leakage (e.g., 'email1@example.com email2@example.com'). A fix was implemented in 2.2.362.

fix

Upgrade to version 2.2.362 or later. Ensure thorough testing of anonymization output, especially for texts with repeated PII entities separated by whitespace.

affects: <2.2.362

gotchaWhile `presidio-anonymizer` is a standalone package, a complete PII detection and anonymization pipeline typically requires `presidio-analyzer` for detection and an underlying NLP engine (like spaCy with a language model such as `en_core_web_lg`). Not installing these dependencies will prevent the full workflow from functioning.

fix

For a complete solution, install `presidio-analyzer` and a spaCy language model using `pip install presidio-analyzer "spacy[en]"` and `python -m spacy download en_core_web_lg`.

affects: All versions

Errors

Common errors & fixes

ImportError: cannot import name 'AnonymizerConfig' from 'presidio_anonymizer'

The `AnonymizerConfig` class has been deprecated or moved to a different location in recent versions of `presidio-anonymizer`.

fix

Instead of `AnonymizerConfig`, you should typically use `OperatorConfig` or directly define operator parameters within the `anonymize` method. Remove the import for `AnonymizerConfig` and adjust your code to use `OperatorConfig` for configuring anonymization operators, if needed.

AttributeError: 'RecognizerResult' object has no attribute 'has_conflict'

This error often occurs due to an incompatibility between the versions of `presidio-analyzer` and `presidio-anonymizer`, where the `RecognizerResult` object structure or its methods have changed.

fix

Ensure both `presidio-analyzer` and `presidio-anonymizer` are updated to compatible versions, ideally the latest stable releases, by running `pip install --upgrade presidio-analyzer presidio-anonymizer`.

ModuleNotFoundError: No module named 'presidio_anonymizer'

The `presidio-anonymizer` package is not installed in your Python environment or the environment is not correctly activated.

fix

Install the package using pip: `pip install presidio-anonymizer`. If you also use the analyzer, install both: `pip install presidio-analyzer presidio-anonymizer`. Additionally, remember to download a spaCy model if using the default NLP engine: `python -m spacy download en_core_web_lg`.

AnonymizerEngine only anonymizes first entity when multiple entities are space-separated

By default, `AnonymizerEngine` might merge space-separated entities of the same type, leading to only the first occurrence being anonymized, and subsequent entities being exposed.

fix

To prevent this behavior, pass the `merge_whitespace_entities=False` parameter to the `anonymize` method of `AnonymizerEngine`.

Upgrade

Version history

2.2.364latest on PyPI · released Jul 22, 2026

Audit

Dependencies

pythonrequiredRequired Python version.

presidio-analyzeroptionalOften used in conjunction for PII detection before anonymization. Provides the RecognizerResult objects needed by AnonymizerEngine.

spacyoptionalRequired by presidio-analyzer for NLP capabilities and language models (e.g., en_core_web_lg) to detect PII.

Agent activity

19 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/Microsoft/presidio ↗

packagehttps://pypi.org/project/presidio-anonymizer/ ↗

homepagehttps://microsoft.github.io/presidio/ ↗