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Presidio Anonymizer API Reference

Anonymizer root module.

AnonymizerEngine

Bases: EngineBase

AnonymizerEngine class.

Handles the entire logic of the Presidio-anonymizer. Gets the original text and replaces the PII entities with the desired anonymizers.

Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/anonymizer_engine.py 
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class AnonymizerEngine(EngineBase):
    """
    AnonymizerEngine class.

    Handles the entire logic of the Presidio-anonymizer. Gets the original text
    and replaces the PII entities with the desired anonymizers.
    """

    logger = logging.getLogger("presidio-anonymizer")

    def __init__(self):
        EngineBase.__init__(self)

    def anonymize(
            self,
            text: str,
            analyzer_results: List[RecognizerResult],
            operators: Optional[Dict[str, OperatorConfig]] = None,
    ) -> EngineResult:
        """Anonymize method to anonymize the given text.

        :param text: the text we are anonymizing
        :param analyzer_results: A list of RecognizerResult class -> The results we
        received from the analyzer
        :param operators: The configuration of the anonymizers we would like
        to use for each entity e.g.: {"PHONE_NUMBER":OperatorConfig("redact", {})}
        received from the analyzer
        :return: the anonymized text and a list of information about the
        anonymized entities.

        :example:

        >>> from presidio_anonymizer import AnonymizerEngine
        >>> from presidio_anonymizer.entities import RecognizerResult, OperatorConfig

        >>> # Initialize the engine with logger.
        >>> engine = AnonymizerEngine()

        >>> # Invoke the anonymize function with the text, analyzer results and
        >>> # Operators to define the anonymization type.
        >>> result = engine.anonymize(
        >>>     text="My name is Bond, James Bond",
        >>>     analyzer_results=[RecognizerResult(entity_type="PERSON",
        >>>                                        start=11,
        >>>                                        end=15,
        >>>                                        score=0.8),
        >>>                       RecognizerResult(entity_type="PERSON",
        >>>                                        start=17,
        >>>                                        end=27,
        >>>                                        score=0.8)],
        >>>     operators={"PERSON": OperatorConfig("replace", {"new_value": "BIP"})}
        >>> )

        >>> print(result)
        text: My name is BIP, BIP.
        items:
        [
            {'start': 16, 'end': 19, 'entity_type': 'PERSON',
             'text': 'BIP', 'operator': 'replace'},
            {'start': 11, 'end': 14, 'entity_type': 'PERSON',
             'text': 'BIP', 'operator': 'replace'}
        ]


        """
        analyzer_results = self._remove_conflicts_and_get_text_manipulation_data(
            analyzer_results
        )

        operators = self.__check_or_add_default_operator(operators)

        return self._operate(text, analyzer_results, operators, OperatorType.Anonymize)

    def _remove_conflicts_and_get_text_manipulation_data(
            self, analyzer_results: List[RecognizerResult]
    ) -> List[RecognizerResult]:
        """
        Iterate the list and create a sorted unique results list from it.

        Only insert results which are:
        1. Indices are not contained in other result.
        2. Have the same indices as other results but with larger score.
        :return: List
        """
        tmp_analyzer_results = []
        # This list contains all elements which we need to check a single result
        # against. If a result is dropped, it can also be dropped from this list
        # since it is intersecting with another result and we selected the other one.
        other_elements = analyzer_results.copy()
        for result in analyzer_results:
            other_elements.remove(result)

            is_merge_same_entity_type = False
            for other_element in other_elements:
                if other_element.entity_type != result.entity_type:
                    continue
                if result.intersects(other_element) == 0:
                    continue

                other_element.start = min(result.start, other_element.start)
                other_element.end = max(result.end, other_element.end)
                other_element.score = max(result.score, other_element.score)
                is_merge_same_entity_type = True
                break
            if not is_merge_same_entity_type:
                other_elements.append(result)
                tmp_analyzer_results.append(result)
            else:
                self.logger.debug(f"removing element {result} from "
                                  f"results list due to merge")

        unique_text_metadata_elements = []
        # This list contains all elements which we need to check a single result
        # against. If a result is dropped, it can also be dropped from this list
        # since it is intersecting with another result and we selected the other one.
        other_elements = tmp_analyzer_results.copy()
        for result in tmp_analyzer_results:
            other_elements.remove(result)
            result_conflicted = self.__is_result_conflicted_with_other_elements(
                other_elements, result
            )
            if not result_conflicted:
                other_elements.append(result)
                unique_text_metadata_elements.append(result)
            else:
                self.logger.debug(
                    f"removing element {result} from results list due to conflict"
                )
        return unique_text_metadata_elements

    def get_anonymizers(self) -> List[str]:
        """Return a list of supported anonymizers."""
        names = [p for p in self.operators_factory.get_anonymizers().keys()]
        return names

    @staticmethod
    def __is_result_conflicted_with_other_elements(other_elements, result):
        return any(
            [result.has_conflict(other_element) for other_element in other_elements]
        )

    @staticmethod
    def __check_or_add_default_operator(
            operators: Dict[str, OperatorConfig]
    ) -> Dict[str, OperatorConfig]:
        default_operator = OperatorConfig(DEFAULT)
        if not operators:
            return {"DEFAULT": default_operator}
        if not operators.get("DEFAULT"):
            operators["DEFAULT"] = default_operator
        return operators

anonymize(text, analyzer_results, operators=None)

Anonymize method to anonymize the given text.

:example:

from presidio_anonymizer import AnonymizerEngine from presidio_anonymizer.entities import RecognizerResult, OperatorConfig

Initialize the engine with logger.

engine = AnonymizerEngine()

Invoke the anonymize function with the text, analyzer results and

Operators to define the anonymization type.

result = engine.anonymize( text="My name is Bond, James Bond", analyzer_results=[RecognizerResult(entity_type="PERSON", start=11, end=15, score=0.8), RecognizerResult(entity_type="PERSON", start=17, end=27, score=0.8)], operators={"PERSON": OperatorConfig("replace", {"new_value": "BIP"})} )

print(result) text: My name is BIP, BIP. items: [ {'start': 16, 'end': 19, 'entity_type': 'PERSON', 'text': 'BIP', 'operator': 'replace'}, {'start': 11, 'end': 14, 'entity_type': 'PERSON', 'text': 'BIP', 'operator': 'replace'} ]

Parameters:

Name Type Description Default
text str

the text we are anonymizing

 required
analyzer_results List[RecognizerResult]

A list of RecognizerResult class -> The results we received from the analyzer

 required
operators Optional[Dict[str, OperatorConfig]]

The configuration of the anonymizers we would like to use for each entity e.g.: {"PHONE_NUMBER":OperatorConfig("redact", {})} received from the analyzer

 None

Returns:

Type Description
EngineResult

the anonymized text and a list of information about the anonymized entities.
Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/anonymizer_engine.py 
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def anonymize(
        self,
        text: str,
        analyzer_results: List[RecognizerResult],
        operators: Optional[Dict[str, OperatorConfig]] = None,
) -> EngineResult:
    """Anonymize method to anonymize the given text.

    :param text: the text we are anonymizing
    :param analyzer_results: A list of RecognizerResult class -> The results we
    received from the analyzer
    :param operators: The configuration of the anonymizers we would like
    to use for each entity e.g.: {"PHONE_NUMBER":OperatorConfig("redact", {})}
    received from the analyzer
    :return: the anonymized text and a list of information about the
    anonymized entities.

    :example:

    >>> from presidio_anonymizer import AnonymizerEngine
    >>> from presidio_anonymizer.entities import RecognizerResult, OperatorConfig

    >>> # Initialize the engine with logger.
    >>> engine = AnonymizerEngine()

    >>> # Invoke the anonymize function with the text, analyzer results and
    >>> # Operators to define the anonymization type.
    >>> result = engine.anonymize(
    >>>     text="My name is Bond, James Bond",
    >>>     analyzer_results=[RecognizerResult(entity_type="PERSON",
    >>>                                        start=11,
    >>>                                        end=15,
    >>>                                        score=0.8),
    >>>                       RecognizerResult(entity_type="PERSON",
    >>>                                        start=17,
    >>>                                        end=27,
    >>>                                        score=0.8)],
    >>>     operators={"PERSON": OperatorConfig("replace", {"new_value": "BIP"})}
    >>> )

    >>> print(result)
    text: My name is BIP, BIP.
    items:
    [
        {'start': 16, 'end': 19, 'entity_type': 'PERSON',
         'text': 'BIP', 'operator': 'replace'},
        {'start': 11, 'end': 14, 'entity_type': 'PERSON',
         'text': 'BIP', 'operator': 'replace'}
    ]


    """
    analyzer_results = self._remove_conflicts_and_get_text_manipulation_data(
        analyzer_results
    )

    operators = self.__check_or_add_default_operator(operators)

    return self._operate(text, analyzer_results, operators, OperatorType.Anonymize)

get_anonymizers()

Return a list of supported anonymizers.

Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/anonymizer_engine.py 
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def get_anonymizers(self) -> List[str]:
    """Return a list of supported anonymizers."""
    names = [p for p in self.operators_factory.get_anonymizers().keys()]
    return names

BatchAnonymizerEngine

BatchAnonymizerEngine class.

A class that provides functionality to anonymize in batches.

Parameters:

Name Type Description Default
anonymizer_engine Optional[AnonymizerEngine]

An instance of the AnonymizerEngine class.

 None
Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/batch_anonymizer_engine.py 
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class BatchAnonymizerEngine:
    """
    BatchAnonymizerEngine class.

    A class that provides functionality to anonymize in batches.
    :param anonymizer_engine: An instance of the AnonymizerEngine class.
    """

    def __init__(self, anonymizer_engine: Optional[AnonymizerEngine] = None):
        self.anonymizer_engine = anonymizer_engine or AnonymizerEngine()

    def anonymize_list(
        self,
        texts: List[Union[str, bool, int, float]],
        recognizer_results_list: List[List[RecognizerResult]],
        **kwargs
    ) -> List[EngineResult]:
        """
        Anonymize a list of strings.

        :param texts: List containing the texts to be anonymized (original texts)
        :param recognizer_results_list: A list of lists of RecognizerResult,
        the output of the AnalyzerEngine on each text in the list.
        :param kwargs: Additional kwargs for the `AnonymizerEngine.anonymize` method
        """
        return_list = []
        if not recognizer_results_list:
            recognizer_results_list = [[] for _ in range(len(texts))]
        for text, recognizer_results in zip(texts, recognizer_results_list):
            if type(text) in (str, bool, int, float):
                res = self.anonymizer_engine.anonymize(
                    text=str(text), analyzer_results=recognizer_results, **kwargs
                )
                return_list.append(res.text)
            else:
                return_list.append(text)

        return return_list

    def anonymize_dict(
        self, analyzer_results: Iterable[DictRecognizerResult], **kwargs
    ) -> Dict[str, str]:
        """
        Anonymize values in a dictionary.

        :param analyzer_results: Iterator of `DictRecognizerResult`
        containing the output of the AnalyzerEngine.analyze_dict on the input text.
        :param kwargs: Additional kwargs for the `AnonymizerEngine.anonymize` method
        """

        return_dict = {}
        for result in analyzer_results:
            if isinstance(result.value, dict):
                resp = self.anonymize_dict(
                    analyzer_results=result.recognizer_results, **kwargs
                )
                return_dict[result.key] = resp

            elif isinstance(result.value, str):
                resp = self.anonymizer_engine.anonymize(
                    text=result.value,
                    analyzer_results=result.recognizer_results,
                    **kwargs
                )
                return_dict[result.key] = resp.text

            elif isinstance(result.value, collections.abc.Iterable):
                anonymize_response = self.anonymize_list(
                    texts=result.value,
                    recognizer_results_list=result.recognizer_results,
                    **kwargs
                )
                return_dict[result.key] = anonymize_response
            else:
                return_dict[result.key] = result.value
        return return_dict

anonymize_dict(analyzer_results, **kwargs)

Anonymize values in a dictionary.

Parameters:

Name Type Description Default
analyzer_results Iterable[DictRecognizerResult]

Iterator of DictRecognizerResult containing the output of the AnalyzerEngine.analyze_dict on the input text.

 required
kwargs

Additional kwargs for the AnonymizerEngine.anonymize method

 {}
Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/batch_anonymizer_engine.py 
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def anonymize_dict(
    self, analyzer_results: Iterable[DictRecognizerResult], **kwargs
) -> Dict[str, str]:
    """
    Anonymize values in a dictionary.

    :param analyzer_results: Iterator of `DictRecognizerResult`
    containing the output of the AnalyzerEngine.analyze_dict on the input text.
    :param kwargs: Additional kwargs for the `AnonymizerEngine.anonymize` method
    """

    return_dict = {}
    for result in analyzer_results:
        if isinstance(result.value, dict):
            resp = self.anonymize_dict(
                analyzer_results=result.recognizer_results, **kwargs
            )
            return_dict[result.key] = resp

        elif isinstance(result.value, str):
            resp = self.anonymizer_engine.anonymize(
                text=result.value,
                analyzer_results=result.recognizer_results,
                **kwargs
            )
            return_dict[result.key] = resp.text

        elif isinstance(result.value, collections.abc.Iterable):
            anonymize_response = self.anonymize_list(
                texts=result.value,
                recognizer_results_list=result.recognizer_results,
                **kwargs
            )
            return_dict[result.key] = anonymize_response
        else:
            return_dict[result.key] = result.value
    return return_dict

anonymize_list(texts, recognizer_results_list, **kwargs)

Anonymize a list of strings.

Parameters:

Name Type Description Default
texts List[Union[str, bool, int, float]]

List containing the texts to be anonymized (original texts)

 required
recognizer_results_list List[List[RecognizerResult]]

A list of lists of RecognizerResult, the output of the AnalyzerEngine on each text in the list.

 required
kwargs

Additional kwargs for the AnonymizerEngine.anonymize method

 {}
Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/batch_anonymizer_engine.py 
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def anonymize_list(
    self,
    texts: List[Union[str, bool, int, float]],
    recognizer_results_list: List[List[RecognizerResult]],
    **kwargs
) -> List[EngineResult]:
    """
    Anonymize a list of strings.

    :param texts: List containing the texts to be anonymized (original texts)
    :param recognizer_results_list: A list of lists of RecognizerResult,
    the output of the AnalyzerEngine on each text in the list.
    :param kwargs: Additional kwargs for the `AnonymizerEngine.anonymize` method
    """
    return_list = []
    if not recognizer_results_list:
        recognizer_results_list = [[] for _ in range(len(texts))]
    for text, recognizer_results in zip(texts, recognizer_results_list):
        if type(text) in (str, bool, int, float):
            res = self.anonymizer_engine.anonymize(
                text=str(text), analyzer_results=recognizer_results, **kwargs
            )
            return_list.append(res.text)
        else:
            return_list.append(text)

    return return_list

DeanonymizeEngine

Bases: EngineBase

Deanonymize text that was previously anonymized.

Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/deanonymize_engine.py 
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class DeanonymizeEngine(EngineBase):
    """Deanonymize text that was previously anonymized."""

    def __init__(self):
        self.logger = logging.getLogger("presidio-anonymizer")
        EngineBase.__init__(self)

    def deanonymize(
        self,
        text: str,
        entities: List[OperatorResult],
        operators: Dict[str, OperatorConfig],
    ) -> EngineResult:
        """
        Receive the text, entities and operators to perform deanonymization over.

        :param operators: the operators to apply on the anonymizer result entities
        :param text: the full text with the encrypted entities
        :param entities: list of encrypted entities
        :return: EngineResult - the new text and data about the deanonymized entities.
        """
        return self._operate(text, entities, operators, OperatorType.Deanonymize)

    def get_deanonymizers(self) -> List[str]:
        """Return a list of supported deanonymizers."""
        names = [p for p in self.operators_factory.get_deanonymizers().keys()]
        return names

deanonymize(text, entities, operators)

Receive the text, entities and operators to perform deanonymization over.

Parameters:

Name Type Description Default
operators Dict[str, OperatorConfig]

the operators to apply on the anonymizer result entities

 required
text str

the full text with the encrypted entities

 required
entities List[OperatorResult]

list of encrypted entities

 required

Returns:

Type Description
EngineResult

EngineResult - the new text and data about the deanonymized entities.
Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/deanonymize_engine.py 
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def deanonymize(
    self,
    text: str,
    entities: List[OperatorResult],
    operators: Dict[str, OperatorConfig],
) -> EngineResult:
    """
    Receive the text, entities and operators to perform deanonymization over.

    :param operators: the operators to apply on the anonymizer result entities
    :param text: the full text with the encrypted entities
    :param entities: list of encrypted entities
    :return: EngineResult - the new text and data about the deanonymized entities.
    """
    return self._operate(text, entities, operators, OperatorType.Deanonymize)

get_deanonymizers()

Return a list of supported deanonymizers.

Source code in /opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/presidio_anonymizer/deanonymize_engine.py 
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def get_deanonymizers(self) -> List[str]:
    """Return a list of supported deanonymizers."""
    names = [p for p in self.operators_factory.get_deanonymizers().keys()]
    return names