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Add data anonymizer (#9863)

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### Description

The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.

Anonynization consists of two steps:

1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.

We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.

### Future works

- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.

### Twitter handle
@deepsense_ai / @MaksOpp

---------

Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
maks-operlejn-ds
2023-08-30 19:39:44 +02:00
committed by GitHub
parent 98cce7dcd3
commit a8f804a618
10 changed files with 1543 additions and 2 deletions
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3
libs/experimental/Makefile
View File

@@ -15,6 +15,9 @@ tests:
test_watch:
poetry run ptw --now . -- tests/unit_tests
extended_tests:
poetry run pytest --only-extended tests/unit_tests
######################
# LINTING AND FORMATTING

4
libs/experimental/langchain_experimental/data_anonymizer/__init__.py Normal file
View File

@@ -0,0 +1,4 @@
"""Data anonymizer package"""
from langchain_experimental.data_anonymizer.presidio import PresidioAnonymizer
__all__ = ["PresidioAnonymizer"]

17
libs/experimental/langchain_experimental/data_anonymizer/base.py Normal file
View File

@@ -0,0 +1,17 @@
from abc import ABC, abstractmethod
class AnonymizerBase(ABC):
"""
Base abstract class for anonymizers.
It is public and non-virtual because it allows
wrapping the behavior for all methods in a base class.
"""
def anonymize(self, text: str) -> str:
"""Anonymize text"""
return self._anonymize(text)
@abstractmethod
def _anonymize(self, text: str) -> str:
"""Abstract method to anonymize text"""

40
libs/experimental/langchain_experimental/data_anonymizer/faker_presidio_mapping.py Normal file
View File

@@ -0,0 +1,40 @@
import string
from typing import Callable, Dict
def get_pseudoanonymizer_mapping() -> Dict[str, Callable]:
try:
from faker import Faker
except ImportError as e:
raise ImportError(
"Could not import faker, please install it with `pip install Faker`."
) from e
fake = Faker()
# Listed entities supported by Microsoft Presidio (for now, global and US only)
# Source: https://microsoft.github.io/presidio/supported_entities/
return {
# Global entities
"PERSON": lambda _: fake.name(),
"EMAIL_ADDRESS": lambda _: fake.email(),
"PHONE_NUMBER": lambda _: fake.phone_number(),
"IBAN_CODE": lambda _: fake.iban(),
"CREDIT_CARD": lambda _: fake.credit_card_number(),
"CRYPTO": lambda _: "bc1"
+ "".join(
fake.random_choices(string.ascii_lowercase + string.digits, length=26)
),
"IP_ADDRESS": lambda _: fake.ipv4_public(),
"LOCATION": lambda _: fake.address(),
"DATE_TIME": lambda _: fake.iso8601(),
"NRP": lambda _: str(fake.random_number(digits=8, fix_len=True)),
"MEDICAL_LICENSE": lambda _: fake.bothify(text="??######").upper(),
"URL": lambda _: fake.url(),
# US-specific entities
"US_BANK_NUMBER": lambda _: fake.bban(),
"US_DRIVER_LICENSE": lambda _: str(fake.random_number(digits=9, fix_len=True)),
"US_ITIN": lambda _: fake.bothify(text="9##-7#-####"),
"US_PASSPORT": lambda _: fake.bothify(text="#####??").upper(),
"US_SSN": lambda _: fake.ssn(),
}

91
libs/experimental/langchain_experimental/data_anonymizer/presidio.py Normal file
View File

@@ -0,0 +1,91 @@
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, List, Optional
from langchain_experimental.data_anonymizer.base import AnonymizerBase
from langchain_experimental.data_anonymizer.faker_presidio_mapping import (
get_pseudoanonymizer_mapping,
)
if TYPE_CHECKING:
from presidio_analyzer import EntityRecognizer
from presidio_anonymizer.entities import OperatorConfig
class PresidioAnonymizer(AnonymizerBase):
"""Anonymizer using Microsoft Presidio."""
def __init__(
self,
analyzed_fields: Optional[List[str]] = None,
language: str = "en",
operators: Optional[Dict[str, OperatorConfig]] = None,
):
"""
Args:
analyzed_fields: List of fields to detect and then anonymize.
Defaults to all entities supported by Microsoft Presidio.
language: Language to use for analysis. Defaults to english.
operators: Operators to use for anonymization.
Operators allow for custom anonymization of detected PII.
Learn more:
https://microsoft.github.io/presidio/tutorial/10_simple_anonymization/
"""
try:
from presidio_analyzer import AnalyzerEngine
except ImportError as e:
raise ImportError(
"Could not import presidio_analyzer, please install with "
"`pip install presidio-analyzer`. You will also need to download a "
"spaCy model to use the analyzer, e.g. "
"`python -m spacy download en_core_web_lg`."
) from e
try:
from presidio_anonymizer import AnonymizerEngine
from presidio_anonymizer.entities import OperatorConfig
except ImportError as e:
raise ImportError(
"Could not import presidio_anonymizer, please install with "
"`pip install presidio-anonymizer`."
) from e
self.analyzed_fields = (
analyzed_fields
if analyzed_fields is not None
else list(get_pseudoanonymizer_mapping().keys())
)
self.language = language
self.operators = (
operators
if operators is not None
else {
field: OperatorConfig(
operator_name="custom", params={"lambda": faker_function}
)
for field, faker_function in get_pseudoanonymizer_mapping().items()
}
)
self._analyzer = AnalyzerEngine()
self._anonymizer = AnonymizerEngine()
def _anonymize(self, text: str) -> str:
results = self._analyzer.analyze(
text,
entities=self.analyzed_fields,
language=self.language,
)
return self._anonymizer.anonymize(
text,
analyzer_results=results,
operators=self.operators,
).text
def add_recognizer(self, recognizer: EntityRecognizer) -> None:
"""Add a recognizer to the analyzer"""
self._analyzer.registry.add_recognizer(recognizer)
self.analyzed_fields.extend(recognizer.supported_entities)
def add_operators(self, operators: Dict[str, OperatorConfig]) -> None:
"""Add operators to the anonymizer"""
self.operators.update(operators)

776
libs/experimental/poetry.lock generated
View File

@@ -392,6 +392,60 @@ webencodings = "*"
[package.extras]
css = ["tinycss2 (>=1.1.0,<1.2)"]
[[package]]
name = "blis"
version = "0.7.10"
description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension."
optional = true
python-versions = "*"
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[package.dependencies]
numpy = [
{version = ">=1.15.0", markers = "python_version < \"3.9\""},
{version = ">=1.19.0", markers = "python_version >= \"3.9\""},
]
[[package]]
name = "catalogue"
version = "2.0.9"
description = "Super lightweight function registries for your library"
optional = true
python-versions = ">=3.6"
files = [
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]
[[package]]
name = "certifi"
version = "2023.7.22"
@@ -607,6 +661,58 @@ lint = ["black (>=22.6.0)", "mdformat (>0.7)", "mdformat-gfm (>=0.3.5)", "ruff (
test = ["pytest"]
typing = ["mypy (>=0.990)"]
[[package]]
name = "confection"
version = "0.1.1"
description = "The sweetest config system for Python"
optional = true
python-versions = ">=3.6"
files = [
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]
[package.dependencies]
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0"
srsly = ">=2.4.0,<3.0.0"
[[package]]
name = "cymem"
version = "2.0.7"
description = "Manage calls to calloc/free through Cython"
optional = true
python-versions = "*"
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[[package]]
name = "dataclasses-json"
version = "0.5.9"
@@ -703,6 +809,21 @@ files = [
[package.extras]
tests = ["asttokens", "littleutils", "pytest", "rich"]
[[package]]
name = "faker"
version = "19.3.1"
description = "Faker is a Python package that generates fake data for you."
optional = true
python-versions = ">=3.8"
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[[package]]
name = "stack-data"
version = "0.6.2"
@@ -2922,6 +3541,84 @@ tornado = ">=6.1.0"
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
[[package]]
name = "thinc"
version = "8.1.12"
description = "A refreshing functional take on deep learning, compatible with your favorite libraries"
optional = true
python-versions = ">=3.6"
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blis = ">=0.7.8,<0.8.0"
catalogue = ">=2.0.4,<2.1.0"
confection = ">=0.0.1,<1.0.0"
cymem = ">=2.0.2,<2.1.0"
murmurhash = ">=1.0.2,<1.1.0"
numpy = [
{version = ">=1.15.0", markers = "python_version < \"3.9\""},
{version = ">=1.19.0", markers = "python_version >= \"3.9\""},
]
packaging = ">=20.0"
preshed = ">=3.0.2,<3.1.0"
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0"
setuptools = "*"
srsly = ">=2.4.0,<3.0.0"
wasabi = ">=0.8.1,<1.2.0"
[package.extras]
cuda = ["cupy (>=5.0.0b4)"]
cuda-autodetect = ["cupy-wheel (>=11.0.0)"]
cuda100 = ["cupy-cuda100 (>=5.0.0b4)"]
cuda101 = ["cupy-cuda101 (>=5.0.0b4)"]
cuda102 = ["cupy-cuda102 (>=5.0.0b4)"]
cuda110 = ["cupy-cuda110 (>=5.0.0b4)"]
cuda111 = ["cupy-cuda111 (>=5.0.0b4)"]
cuda112 = ["cupy-cuda112 (>=5.0.0b4)"]
cuda113 = ["cupy-cuda113 (>=5.0.0b4)"]
cuda114 = ["cupy-cuda114 (>=5.0.0b4)"]
cuda115 = ["cupy-cuda115 (>=5.0.0b4)"]
cuda116 = ["cupy-cuda116 (>=5.0.0b4)"]
cuda117 = ["cupy-cuda117 (>=5.0.0b4)"]
cuda11x = ["cupy-cuda11x (>=11.0.0)"]
cuda80 = ["cupy-cuda80 (>=5.0.0b4)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4)"]
datasets = ["ml-datasets (>=0.2.0,<0.3.0)"]
mxnet = ["mxnet (>=1.5.1,<1.6.0)"]
tensorflow = ["tensorflow (>=2.0.0,<2.6.0)"]
torch = ["torch (>=1.6.0)"]
[[package]]
name = "tinycss2"
version = "1.2.1"
@@ -2940,6 +3637,23 @@ webencodings = ">=0.4"
doc = ["sphinx", "sphinx_rtd_theme"]
test = ["flake8", "isort", "pytest"]
[[package]]
name = "tldextract"
version = "3.4.4"
description = "Accurately separates a URL's subdomain, domain, and public suffix, using the Public Suffix List (PSL). By default, this includes the public ICANN TLDs and their exceptions. You can optionally support the Public Suffix List's private domains as well."
optional = true
python-versions = ">=3.7"
files = [
{file = "tldextract-3.4.4-py3-none-any.whl", hash = "sha256:581e7dbefc90e7bb857bb6f768d25c811a3c5f0892ed56a9a2999ddb7b1b70c2"},
{file = "tldextract-3.4.4.tar.gz", hash = "sha256:5fe3210c577463545191d45ad522d3d5e78d55218ce97215e82004dcae1e1234"},
]
[package.dependencies]
filelock = ">=3.0.8"
idna = "*"
requests = ">=2.1.0"
requests-file = ">=1.4"
[[package]]
name = "tomli"
version = "2.0.1"
@@ -2971,6 +3685,26 @@ files = [
{file = "tornado-6.3.3.tar.gz", hash = "sha256:e7d8db41c0181c80d76c982aacc442c0783a2c54d6400fe028954201a2e032fe"},
]
[[package]]
name = "tqdm"
version = "4.66.1"
description = "Fast, Extensible Progress Meter"
optional = true
python-versions = ">=3.7"
files = [
{file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
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]
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras]
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
[[package]]
name = "traitlets"
version = "5.9.0"
@@ -2986,6 +3720,27 @@ files = [
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
[[package]]
name = "typer"
version = "0.9.0"
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
optional = true
python-versions = ">=3.6"
files = [
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]
[package.dependencies]
click = ">=7.1.1,<9.0.0"
typing-extensions = ">=3.7.4.3"
[package.extras]
all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
[[package]]
name = "types-pyyaml"
version = "6.0.12.11"
@@ -3054,6 +3809,20 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "wasabi"
version = "1.1.2"
description = "A lightweight console printing and formatting toolkit"
optional = true
python-versions = ">=3.6"
files = [
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colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\" and python_version >= \"3.7\""}
[[package]]
name = "wcwidth"
version = "0.2.6"
@@ -3220,7 +3989,10 @@ files = [
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
[extras]
extended-testing = ["faker", "presidio-analyzer", "presidio-anonymizer"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "bd737027e0fd9ea2ee823632f89dbd947c7d5f41bb05fc1cbff04106ae3dd350"
content-hash = "66ac482bd05eb74414210ac28fc1e8dae1a9928a4a1314e1326fada3551aa8ad"

13
libs/experimental/pyproject.toml
View File

@@ -11,6 +11,9 @@ repository = "https://github.com/langchain-ai/langchain"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain = ">=0.0.239"
presidio-anonymizer = {version = "^2.2.33", optional = true}
presidio-analyzer = {version = "^2.2.33", optional = true}
faker = {version = "^19.3.1", optional = true}
[tool.poetry.group.lint.dependencies]
@@ -31,6 +34,16 @@ setuptools = "^67.6.1"
# Any dependencies that do not meet that criteria will be removed.
pytest = "^7.3.0"
# An extra used to be able to add extended testing.
# Please use new-line on formatting to make it easier to add new packages without
# merge-conflicts
[tool.poetry.extras]
extended_testing = [
"presidio-anonymizer",
"presidio-analyzer",
"faker",
]
[tool.ruff]
select = [
"E", # pycodestyle

84
libs/experimental/tests/unit_tests/test_data_anonymizer.py Normal file
View File

@@ -0,0 +1,84 @@
from typing import Iterator, List
import pytest
@pytest.fixture(scope="module", autouse=True)
def check_spacy_model() -> Iterator[None]:
import spacy
if not spacy.util.is_package("en_core_web_lg"):
pytest.skip(reason="Spacy model 'en_core_web_lg' not installed")
yield
@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
@pytest.mark.parametrize(
"analyzed_fields,should_contain",
[(["PERSON"], False), (["PHONE_NUMBER"], True), (None, False)],
)
def test_anonymize(analyzed_fields: List[str], should_contain: bool) -> None:
"""Test anonymizing a name in a simple sentence"""
from langchain_experimental.data_anonymizer import PresidioAnonymizer
text = "Hello, my name is John Doe."
anonymizer = PresidioAnonymizer(analyzed_fields=analyzed_fields)
anonymized_text = anonymizer.anonymize(text)
assert ("John Doe" in anonymized_text) == should_contain
@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
def test_anonymize_multiple() -> None:
"""Test anonymizing multiple items in a sentence"""
from langchain_experimental.data_anonymizer import PresidioAnonymizer
text = "John Smith's phone number is 313-666-7440 and email is johnsmith@gmail.com"
anonymizer = PresidioAnonymizer()
anonymized_text = anonymizer.anonymize(text)
for phrase in ["John Smith", "313-666-7440", "johnsmith@gmail.com"]:
assert phrase not in anonymized_text
@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
def test_anonymize_with_custom_operator() -> None:
"""Test anonymize a name with a custom operator"""
from presidio_anonymizer.entities import OperatorConfig
from langchain_experimental.data_anonymizer import PresidioAnonymizer
custom_operator = {"PERSON": OperatorConfig("replace", {"new_value": "<name>"})}
anonymizer = PresidioAnonymizer(operators=custom_operator)
text = "Jane Doe was here."
anonymized_text = anonymizer.anonymize(text)
assert anonymized_text == "<name> was here."
@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
def test_add_recognizer_operator() -> None:
"""
Test add recognizer and anonymize a new type of entity and with a custom operator
"""
from presidio_analyzer import PatternRecognizer
from presidio_anonymizer.entities import OperatorConfig
from langchain_experimental.data_anonymizer import PresidioAnonymizer
anonymizer = PresidioAnonymizer(analyzed_fields=[])
titles_list = ["Sir", "Madam", "Professor"]
custom_recognizer = PatternRecognizer(
supported_entity="TITLE", deny_list=titles_list
)
anonymizer.add_recognizer(custom_recognizer)
# anonymizing with custom recognizer
text = "Madam Jane Doe was here."
anonymized_text = anonymizer.anonymize(text)
assert anonymized_text == "<TITLE> Jane Doe was here."
# anonymizing with custom recognizer and operator
custom_operator = {"TITLE": OperatorConfig("replace", {"new_value": "Dear"})}
anonymizer.add_operators(custom_operator)
anonymized_text = anonymizer.anonymize(text)
assert anonymized_text == "Dear Jane Doe was here."