Open-source PII Detection & Anonymization.

Installation

DataFog can be installed via pip:

pip install datafog

CLI

📚 Quick Reference

🔍 Detailed Usage

Scanning Text

To scan and annotate text for PII entities:

datafog scan-text "Your text here"

Example:

datafog scan-text "Tim Cook is the CEO of Apple and is based out of Cupertino, California"

Scanning Images

To extract text from images and optionally perform PII annotation:

datafog scan-image "path/to/image.png" --operations extract

Example:

datafog scan-image "nokia-statement.png" --operations extract

To extract text and annotate PII:

datafog scan-image "nokia-statement.png" --operations scan

Redacting Text

To redact PII in text:

datafog redact-text "Tim Cook is the CEO of Apple and is based out of Cupertino, California"

which should output:

[REDACTED] is the CEO of [REDACTED] and is based out of [REDACTED], [REDACTED]

Replacing Text

To replace detected PII:

datafog replace-text "Tim Cook is the CEO of Apple and is based out of Cupertino, California"

which should return something like:

[PERSON_B86CACE6] is the CEO of [UNKNOWN_445944D7] and is based out of [UNKNOWN_32BA5DCA], [UNKNOWN_B7DF4969]

Note: a unique randomly generated identifier is created for each detected entity

Hashing Text

You can select from SHA256, SHA3-256, and MD5 hashing algorithms to hash detected PII. Currently the hashed output does not match the length of the original entity, for privacy-preserving purposes. The default is SHA256.

datafog hash-text "Tim Cook is the CEO of Apple and is based out of Cupertino, California"

generating an output which looks like this:

5738a37f0af81594b8a8fd677e31b5e2cabd6d7791c89b9f0a1c233bb563ae39 is the CEO of f223faa96f22916294922b171a2696d868fd1f9129302eb41a45b2a2ea2ebbfd and is based out of ab5f41f04096cf7cd314357c4be26993eeebc0c094ca668506020017c35b7a9c, cad0535decc38b248b40e7aef9a1cfd91ce386fa5c46f05ea622649e7faf18fb

Utility Commands

🏥 Health Check

datafog health

⚙️ Show Configuration

datafog show-config

📥 Download Model

datafog download-model en_core_web_sm

📂 Show Model Directory

datafog show-spacy-model-directory en_core_web_sm

📋 List Models

datafog list-spacy-models

🏷️ List Entities

datafog list-entities

⚠️ Important Notes

• For scan-image and scan-text commands, use --operations to specify different operations. Default is scan.
• Process multiple images or text strings in a single command by providing multiple arguments.
• Ensure proper permissions and configuration of the DataFog service before running commands.

💡 Tip: For more detailed information on each command, use the --help option, e.g., datafog scan-text --help.

Python SDK

Getting Started

To use DataFog, you'll need to create a DataFog client with the desired operations. Here's a basic setup:

from datafog import DataFog

# For text annotation
client = DataFog(operations="scan")

# For OCR (Optical Character Recognition)
ocr_client = DataFog(operations="extract")

Text PII Annotation

Here's an example of how to annotate PII in a text document:

import requests

# Fetch sample medical record
doc_url = "https://gist.githubusercontent.com/sidmohan0/b43b72693226422bac5f083c941ecfdb/raw/b819affb51796204d59987893f89dee18428ed5d/note1.txt"
response = requests.get(doc_url)
text_lines = [line for line in response.text.splitlines() if line.strip()]

# Run annotation
annotations = client.run_text_pipeline_sync(str_list=text_lines)
print(annotations)

OCR PII Annotation

For OCR capabilities, you can use the following:

import asyncio
import nest_asyncio

nest_asyncio.apply()

async def run_ocr_pipeline_demo():
    image_url = "https://s3.amazonaws.com/thumbnails.venngage.com/template/dc377004-1c2d-49f2-8ddf-d63f11c8d9c2.png"
    results = await ocr_client.run_ocr_pipeline(image_urls=[image_url])
    print("OCR Pipeline Results:", results)

loop = asyncio.get_event_loop()
loop.run_until_complete(run_ocr_pipeline_demo())

Note: The DataFog library uses asynchronous programming for OCR, so make sure to use the async/await syntax when calling the appropriate methods.

Text Anonymization

DataFog provides various anonymization techniques to protect sensitive information. Here are examples of how to use them:

Redacting Text

To redact PII in text:

from datafog import DataFog
from datafog.config import OperationType

client = DataFog(operations=[OperationType.SCAN, OperationType.REDACT])

text = "Tim Cook is the CEO of Apple and is based out of Cupertino, California"
redacted_text = client.run_text_pipeline_sync([text])[0]
print(redacted_text)

Output:

[REDACTED] is the CEO of [REDACTED] and is based out of [REDACTED], [REDACTED]

Replacing Text

To replace detected PII with unique identifiers:

from datafog import DataFog
from datafog.config import OperationType

client = DataFog(operations=[OperationType.SCAN, OperationType.REPLACE])

text = "Tim Cook is the CEO of Apple and is based out of Cupertino, California"
replaced_text = client.run_text_pipeline_sync([text])[0]
print(replaced_text)

Output:

[PERSON_B86CACE6] is the CEO of [UNKNOWN_445944D7] and is based out of [UNKNOWN_32BA5DCA], [UNKNOWN_B7DF4969]

Hashing Text

To hash detected PII:

from datafog import DataFog
from datafog.config import OperationType
from datafog.models.anonymizer import HashType

client = DataFog(operations=[OperationType.SCAN, OperationType.HASH], hash_type=HashType.SHA256)

text = "Tim Cook is the CEO of Apple and is based out of Cupertino, California"
hashed_text = client.run_text_pipeline_sync([text])[0]
print(hashed_text)

Output:

5738a37f0af81594b8a8fd677e31b5e2cabd6d7791c89b9f0a1c233bb563ae39 is the CEO of f223faa96f22916294922b171a2696d868fd1f9129302eb41a45b2a2ea2ebbfd and is based out of ab5f41f04096cf7cd314357c4be26993eeebc0c094ca668506020017c35b7a9c, cad0535decc38b248b40e7aef9a1cfd91ce386fa5c46f05ea622649e7faf18fb

You can choose from SHA256 (default), SHA3-256, and MD5 hashing algorithms by specifying the hash_type parameter

Examples

For more detailed examples, check out our Jupyter notebooks in the examples/ directory:

• text_annotation_example.ipynb: Demonstrates text PII annotation
• image_processing.ipynb: Shows OCR capabilities and text extraction from images

These notebooks provide step-by-step guides on how to use DataFog for various tasks.

Dev Notes

For local development:

1. Clone the repository.
2. Navigate to the project directory:

   cd datafog-python

3. Create a new virtual environment (using .venv is recommended as it is hardcoded in the justfile):

   python -m venv .venv

4. Activate the virtual environment:
   • On Windows:

     .venv\Scripts\activate

   • On macOS/Linux:

     source .venv/bin/activate

5. Install the package in editable mode:

   pip install -r requirements-dev.txt

6. Set up the project:

   just setup

Now, you can develop and run the project locally.

Important Actions:

• Format the code:

  just format

  This runs isort to sort imports.

• Lint the code:

  just lint

  This runs flake8 to check for linting errors.

• Generate coverage report:

  just coverage-html

  This runs pytest and generates a coverage report in the htmlcov/ directory.

We use pre-commit to run checks locally before committing changes. Once installed, you can run:

pre-commit run --all-files

Dependencies

For OCR, we use Tesseract, which is incorporated into the build step. You can find the relevant configurations under .github/workflows/ in the following files:

• dev-cicd.yml
• feature-cicd.yml
• main-cicd.yml

Testing

• Python 3.10

License

This software is published under the MIT license.