Privacy Engineering with LLMs: Safeguarding PII While Enhancing Text with Privacy Shield
In the age of AI-powered tools and intelligent systems, the ability to search, analyze, and generate insights from documents has become paramount.
Senthilnathan Karuppaiah
Introduction
In today’s data-driven environment, the use of Large Language Models (LLMs) for generative AI and text processing is not just important but increasingly common. With tools like ChatGPT, Google Gemini, Claude.ai, Perplexity and open-source AI models like Llama, Mistral.ai etc. people now rely on LLMs for tasks ranging from email proofreading to refining documents like legal contracts and research papers. However, this widespread adoption also brings a critical challenge: How do we protect Personally Identifiable Information (PII) within these workflows?
Privacy Shield is an open-source utility I created to address the increasing need for protecting Personally Identifiable Information (PII) when working with Large Language Models (LLMs). It builds directly on the code developed by LangChain and Microsoft Presidio, which has been ported as a Python API and designed to be consumed as an API or microservice by frontend applications. It provides a structured approach to anonymize sensitive data, enhance text, and enable reversible de-anonymization, ensuring that sensitive information remains secure throughout the processing workflow.
In this article, I explore the core technology, implementation details, and benefits of incorporating Privacy Shield into workflows that handle sensitive data, along with practical guidance and real-world examples.
Use Case Scenario
Protecting Sensitive Data
Consider a scenario where a company is processing legal documents or customer logs that contain PII. The goal is to use LLMs to refine the text, improve clarity, or extract insights. However, directly exposing sensitive data to external services poses a privacy risk.
With Privacy Shield, the organization can:
::list{type="success"}
- Replace PII with realistic synthetic data.
- Use LLMs to enhance text.
- Restore the original PII after processing. ::
This ensures compliance with privacy regulations and eliminates the risk of data leaks.
Technology Stack
Privacy Shield combines several modern technologies to deliver a seamless and secure text-processing workflow:
::list{type="success"}
- Presidio: An open-source library for detecting and anonymizing PII, supporting over 100 PII types.
- LangChain: A framework for building LLM-powered applications, used here for reversible anonymization and LLM integration.
- OpenAI or Ollama API: Provides access to powerful LLMs like GPT-3.5-turbo or Llama3.2 for text processing.
- Python: The core programming language for the project's backend logic and component integration.
- FastAPI: A high-performance web framework for building the REST API. ::
Schematic Flow
Benefits of Privacy Shield
::list{type="success"}
- PII Protection: Keeps sensitive information secure by never exposing it to external services.
- Data Privacy: Uses robust anonymization and de-anonymization techniques.
- Safe Processing: Allows for powerful text enhancement without risking data breaches.
- Flexibility: Supports customization with parameters like faker_seed, prompt_template, and temperature.
- Scalability: Stateless architecture ensures seamless scalability for large datasets.
- Comprehensive Logging: Enables detailed monitoring for auditability. ::
Live Demo
https://privacyshield.vercel.app/
Use these inputs to experiment with the Privacy Shield Tool and observe how Anonymization and Reverse Anonymization work in action.
Example # 1 John Doe submitted an application for a bank loan. His Social Security Number is 123-45-6789. If you need to contact him, call 555-123-4567 or email him at john.doe@example.com.
Example # 2 Emily Johnson recently reported a lost package. The tracking number is 1Z999AA10123456784. Please send updates to her email at emily.johnson@companymail.com or call her at (987) 654-3210.
Example # 3 Dr. Sarah Brown conducted an appointment with patient Alex Smith. The patient's phone number is 312-555-6789, and their email address is alex.smith@example.com.
Example # 4 Mark Anthony purchased a ticket for the flight scheduled on 12/25/2024. His passport number is A1234567, and he can be contacted via email at mark.anthony@gmail.com or phone at +1-234-567-8901.
Example # 5 David Clark placed an order on 11/10/2024 with the credit card number 4111 1111 1111 1111. For queries, contact him at david.clark@shoppingmail.com or call 555-987-6543.
Recorded Screencast
Just in case the tool stops working, you can view the recorded screencast here:
Getting Started
1. Clone the Repository
Clone the Privacy Shield repository and navigate to the project directory:
git clone https://github.com/senthilsweb/privacyshield.git
cd privacyshield
2. Set Up the Environment
Step 1: Create and Activate Virtual Environment
Use Python 3.10:
python3.10 -m venv env
source env/bin/activate
Step 2: Install Dependencies
Upgrade pip and install required packages:
pip install --upgrade pip
pip install fastapi uvicorn langchain langchain-experimental langchain-openai \
presidio-analyzer presidio-anonymizer spacy pydantic python-dotenv faker requests
Or install dependencies from the requirements.txt file:
pip install -r requirements.txt
Step 3: Download SpaCy Model
python -m spacy download en_core_web_lg
Step 4: Create .env from sample.env
OPENAI_API_KEY=your_key_here
DEFAULT_TEMPERATURE=0
#DEFAULT_PROMPT_TEMPLATE="Rewrite this text into an official, short email:\n\n{anonymized_text}"
DEFAULT_PROMPT_TEMPLATE="Rewrite this text into an ServiceNow Incident Ticket:\n\n{anonymized_text}"
LOG_LEVEL=INFO
DEFAULT_MODEL="gpt-3.5-turbo"
❗ The placeholder {anonymized_text} name cannot be modified.
Step 5: Run the API Server
python3.10 main.py
Step 6: Access the API http://localhost:8000
RESTful APIs
1. Detect PII Entities
Endpoint: /detect-pii-entities Method: POST Description: Detects PII entities in the text and replaces them with placeholders.
Request Body Example
{
"text": "Maria Lynch recently lost his wallet. Inside is some cash and his credit card with the number 4838637940262. If you would find it, please call at 7344131647 or write an email here: jamesmichael@example.com. Maria Lynch would be very grateful!",
"temperature": 0.2
}
Response Example
{
"processed_text": "<PERSON> recently lost his wallet. Inside is some cash and his credit card with the number <CREDIT_CARD>. If you would find it, please call at <DATE_TIME> or write an email here: <EMAIL_ADDRESS>. <PERSON> would be very grateful!",
"entities": [
{
"type": "PERSON",
"score": 0.85,
"start": 0,
"end": 11,
"text": "Maria Lynch"
},
{
"type": "CREDIT_CARD",
"score": 1.0,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "US_BANK_NUMBER",
"score": 0.05,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "US_DRIVER_LICENSE",
"score": 0.01,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "DATE_TIME",
"score": 0.85,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "PHONE_NUMBER",
"score": 0.75,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "IN_PAN",
"score": 0.05,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "US_BANK_NUMBER",
"score": 0.05,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "US_DRIVER_LICENSE",
"score": 0.01,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "EMAIL_ADDRESS",
"score": 1.0,
"start": 181,
"end": 205,
"text": "jamesmichael@example.com"
},
{
"type": "URL",
"score": 0.5,
"start": 194,
"end": 205,
"text": "example.com"
},
{
"type": "PERSON",
"score": 0.85,
"start": 207,
"end": 218,
"text": "Maria Lynch"
}
],
"message": "PII entities detected successfully"
}
2. Anonymize and Transform
Endpoint: /anonymize-and-transform Method: POST Description: Anonymizes PII and generates transformed text using an LLM.
Request Body Example
{
"text": "Maria Lynch recently lost his wallet. Inside is some cash and his credit card with the number 4838637940262. If you would find it, please call at 7344131647 or write an email here: jamesmichael@example.com. Maria Lynch would be very grateful!",
"temperature": 0.2,
"prompt_template": "Rewrite this text into an ServiceNow Incident Ticket:\n\n{anonymized_text}"
}
Response Example
{
"original_text": "Maria Lynch recently lost his wallet. Inside is some cash and his credit card with the number 4838637940262. If you would find it, please call at 7344131647 or write an email here: jamesmichael@example.com. Maria Lynch would be very grateful!",
"anonymized_text": "<PERSON> recently lost his wallet. Inside is some cash and his credit card with the number <CREDIT_CARD>. If you would find it, please call at <DATE_TIME> or write an email here: <EMAIL_ADDRESS>. <PERSON> would be very grateful!",
"faked_text": "Jeffrey Curry recently lost his wallet. Inside is some cash and his credit card with the number 4079577328073. If you would find it, please call at 1974-01-25 or write an email here: rodriguezanna@example.org. Jeffrey Curry would be very grateful!",
"faked_processed_text": "\n\nIf you have found Jeffrey Curry's wallet, please contact him at the provided phone number or email address. Thank you for your help in returning his belongings.",
"processed_text": "\n\nIf you have found Maria Lynch's wallet, please contact him at the provided phone number or email address. Thank you for your help in returning his belongings.",
"model_used": "gpt-3.5-turbo",
"temperature_used": 0.2,
"prompt_used": "\n\n{anonymized_text}",
"entities": [
{
"type": "PERSON",
"score": 0.85,
"start": 0,
"end": 11,
"text": "Maria Lynch"
},
{
"type": "CREDIT_CARD",
"score": 1.0,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "US_BANK_NUMBER",
"score": 0.05,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "US_DRIVER_LICENSE",
"score": 0.01,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "DATE_TIME",
"score": 0.85,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "PHONE_NUMBER",
"score": 0.75,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "IN_PAN",
"score": 0.05,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "US_BANK_NUMBER",
"score": 0.05,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "US_DRIVER_LICENSE",
"score": 0.01,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "EMAIL_ADDRESS",
"score": 1.0,
"start": 181,
"end": 205,
"text": "jamesmichael@example.com"
},
{
"type": "URL",
"score": 0.5,
"start": 194,
"end": 205,
"text": "example.com"
},
{
"type": "PERSON",
"score": 0.85,
"start": 207,
"end": 218,
"text": "Maria Lynch"
}
],
"message": "Enhanced text processed successfully"
}
3. Anonymize with Fake Data
Endpoint: /anonymize-with-fake-data Method: POST Description: Detect PII entities and reaplce it with fake data
Request Body Example
{
"text": "Maria Lynch recently lost his wallet. Inside is some cash and his credit card with the number 4838637940262. If you would find it, please call at 7344131647 or write an email here: jamesmichael@example.com. Maria Lynch would be very grateful!",
"temperature": 0.2
}
Response Example
{
"processed_text": "Jeffrey Curry recently lost his wallet. Inside is some cash and his credit card with the number 4079577328073. If you would find it, please call at 1974-01-25 or write an email here: rodriguezanna@example.org. Jeffrey Curry would be very grateful!",
"entities": [
{
"type": "PERSON",
"score": 0.85,
"start": 0,
"end": 11,
"text": "Maria Lynch"
},
{
"type": "CREDIT_CARD",
"score": 1.0,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "US_BANK_NUMBER",
"score": 0.05,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "US_DRIVER_LICENSE",
"score": 0.01,
"start": 94,
"end": 107,
"text": "4838637940262"
},
{
"type": "DATE_TIME",
"score": 0.85,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "PHONE_NUMBER",
"score": 0.75,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "IN_PAN",
"score": 0.05,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "US_BANK_NUMBER",
"score": 0.05,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "US_DRIVER_LICENSE",
"score": 0.01,
"start": 146,
"end": 156,
"text": "7344131647"
},
{
"type": "EMAIL_ADDRESS",
"score": 1.0,
"start": 181,
"end": 205,
"text": "jamesmichael@example.com"
},
{
"type": "URL",
"score": 0.5,
"start": 194,
"end": 205,
"text": "example.com"
},
{
"type": "PERSON",
"score": 0.85,
"start": 207,
"end": 218,
"text": "Maria Lynch"
}
],
"message": "Anonymized with fake data: text processed successfully."
}