anonipy 0.2.0

The data anonymization package

Data anonymization package, supporting different anonymization strategies

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Documentation: https://eriknovak.github.io/anonipy

Source code: https://github.com/eriknovak/anonipy

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The anonipy package is a python package for data anonymization. It is designed to be simple to use and highly customizable, supporting different anonymization strategies. Powered by LLMs.

Requirements

Before starting the project make sure these requirements are available:

• python. The python programming language (v3.8, v3.9, v3.10).

Install

pip install anonipy

Upgrade

pip install anonipy --upgrade

Example

original_text = """\
Medical Record

Patient Name: John Doe
Date of Birth: 15-01-1985
Date of Examination: 20-05-2024
Social Security Number: 123-45-6789

Examination Procedure:
John Doe underwent a routine physical examination. The procedure included measuring vital signs (blood pressure, heart rate, temperature), a comprehensive blood panel, and a cardiovascular stress test. The patient also reported occasional headaches and dizziness, prompting a neurological assessment and an MRI scan to rule out any underlying issues.

Medication Prescribed:

Ibuprofen 200 mg: Take one tablet every 6-8 hours as needed for headache and pain relief.
Lisinopril 10 mg: Take one tablet daily to manage high blood pressure.
Next Examination Date:
15-11-2024
"""

Use the language detector to detect the language of the text:

from anonipy.utils.language_detector import LanguageDetector

language_detector = LanguageDetector()
language = language_detector(original_text)

Prepare the entity extractor and extract the personal infomation from the original text:

from anonipy.anonymize.extractors import NERExtractor

# define the labels to be extracted and anonymized
labels = [
    {"label": "name", "type": "string"},
    {"label": "social security number", "type": "custom"},
    {"label": "date of birth", "type": "date"},
    {"label": "date", "type": "date"},
]

# initialize the NER extractor for the language and labels
extractor = NERExtractor(labels, lang=language, score_th=0.5)

# extract the entities from the original text
doc, entities = extractor(original_text)

# display the entities in the original text
extractor.display(doc)

Use generators to create substitutes for the entities:

from anonipy.anonymize.generators import (
    LLMLabelGenerator,
    DateGenerator,
    NumberGenerator,
)

# initialize the generators
llm_generator = LLMLabelGenerator()
date_generator = DateGenerator()
number_generator = NumberGenerator()

# prepare the anonymization mapping
def anonymization_mapping(text, entity):
    if entity.type == "string":
        return llm_generator.generate(entity, temperature=0.7)
    if entity.label == "date":
        return date_generator.generate(entity, output_gen="MIDDLE_OF_THE_MONTH")
    if entity.label == "date of birth":
        return date_generator.generate(entity, output_gen="MIDDLE_OF_THE_YEAR")
    if entity.label == "social security number":
        return number_generator.generate(entity)
    return "[REDACTED]"

Anonymize the text using the anonymization mapping:

from anonipy.anonymize.strategies import PseudonymizationStrategy

# initialize the pseudonymization strategy
pseudo_strategy = PseudonymizationStrategy(mapping=anonymization_mapping)

# anonymize the original text
anonymized_text, replacements = pseudo_strategy.anonymize(original_text, entities)

Acknowledgements

Anonipy is developed by the Department for Artificial Intelligence at the Jozef Stefan Institute, and other contributors.

The project has received funding from the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101080288 (PREPARE).