Text anonymization in many languages for python3.6+ using Faker.
pip install anonymizationThis example use NamedEntitiesAnonymizer which require spacy and a spacy model.
pip install spacy
python -m spacy download en_core_web_lg>>> from anonymization import Anonymization, AnonymizerChain, EmailAnonymizer, NamedEntitiesAnonymizer
>>> text = "Hi John,\nthanks for you for subscribing to Superprogram, feel free to ask me any question at secret.mail@Superprogram.com \n Superprogram the best program!"
>>> anon = AnonymizerChain(Anonymization('en_US'))
>>> anon.add_anonymizers(EmailAnonymizer, NamedEntitiesAnonymizer('en_core_web_lg'))
>>> anon.anonymize(text)
'Hi Holly,\nthanks for you for subscribing to Ariel, feel free to ask me any question at shanestevenson@gmail.com \n Ariel the best program!'Or make it reversible with pseudonymize:
>>> from anonymization import Anonymization, AnonymizerChain, EmailAnonymizer, NamedEntitiesAnonymizer
>>> text = "Hi John,\nthanks for you for subscribing to Superprogram, feel free to ask me any question at secret.mail@Superprogram.com \n Superprogram the best program!"
>>> anon = AnonymizerChain(Anonymization('en_US'))
>>> anon.add_anonymizers(EmailAnonymizer, NamedEntitiesAnonymizer('en_core_web_lg'))
>>> clean_text, patch = anon.pseudonymize(text)
>>> print(clean_text)
'Christopher, \nthanks for you for subscribing to Audrey, feel free to ask me any question at colemanwesley@hotmail.com \n Audrey the best program!'
revert_text = anon.revert(clean_text, patch)
>>> print(text == revert_text)
trueOur solution supports many languages along with their specific information formats.
For example, we can generate a french phone number:
>>> from anonymization import Anonymization, PhoneNumberAnonymizer
>>>
>>> text = "C'est bien le 0611223344 ton numéro ?"
>>> anon = Anonymization('fr_FR')
>>> phoneAnonymizer = PhoneNumberAnonymizer(anon)
>>> phoneAnonymizer.anonymize(text)
"C'est bien le 0144939332 ton numéro ?"More examples in /examples
| name | lang |
|---|---|
| FilePathAnonymizer | - |
| name | lang |
|---|---|
| EmailAnonymizer | - |
| UriAnonymizer | - |
| MacAddressAnonymizer | - |
| Ipv4Anonymizer | - |
| Ipv6Anonymizer | - |
| name | lang |
|---|---|
| PhoneNumberAnonymizer | 47+ |
| msisdnAnonymizer | 47+ |
| name | lang |
|---|---|
| DateAnonymizer | - |
| name | lang |
|---|---|
| NamedEntitiesAnonymizer | 7+ |
| DictionaryAnonymizer | - |
| SignatureAnonymizer | 7+ |
Custom anonymizers can be easily created to fit your needs:
class CustomAnonymizer():
def __init__(self, anonymization: Anonymization):
self.anonymization = anonymization
def anonymize(self, text: str) -> str:
return modified_text
# or replace by regex patterns in text using a faker provider
return self.anonymization.regex_anonymizer(text, pattern, provider)
# or replace all occurences using a faker provider
return self.anonymization.replace_all(text, matchs, provider)You may also add new faker provider with the helper Anonymization.add_provider(FakerProvider) or access the faker instance directly Anonymization.faker.
This module is benchmarked on synth_dataset from presidio-research and returns accuracy result(0.79) better than Microsoft's solution(0.75)
You can run the benchmark using docker:
docker build . -f ./benchmark/dockerfile -t anonbench
docker run -it --rm --name anonbench anonbenchMIT