https://github.com/agentica5/a5-pii-anonymizer
Desktop App with Built-In LLM for Removing Personal Identifiable Information in Documents
https://github.com/agentica5/a5-pii-anonymizer
ai ai-safety alm anonymisation anonymity anonymization artificial-intelligence desktop-application electron electron-desktop gdpr hipaa linux llm macos personal-identifiable-information pii privacy reasoning windows
Last synced: 7 months ago
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Desktop App with Built-In LLM for Removing Personal Identifiable Information in Documents
- Host: GitHub
- URL: https://github.com/agentica5/a5-pii-anonymizer
- Owner: AgenticA5
- License: mit
- Created: 2025-02-17T22:10:04.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-10-08T10:59:57.000Z (8 months ago)
- Last Synced: 2025-10-08T12:35:27.145Z (8 months ago)
- Topics: ai, ai-safety, alm, anonymisation, anonymity, anonymization, artificial-intelligence, desktop-application, electron, electron-desktop, gdpr, hipaa, linux, llm, macos, personal-identifiable-information, pii, privacy, reasoning, windows
- Language: JavaScript
- Homepage: https://amicus5.com/apps/pa
- Size: 5.96 MB
- Stars: 36
- Watchers: 2
- Forks: 12
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# A5 PII Anonymizer
### Built-In LLM Desktop App Preview:
This repository provides an **Electron** desktop application for **locally anonymizing documents** before sending them to advanced Large Language Models (LLMs). By stripping out personal or identifiable information using a **context-aware** model, you can safely train or query external LLMs (e.g., OpenAI’s o3 model) with minimal privacy risk.
## Motivation
- **PII Removal**: Traditional RegEx-based anonymization often fails on nuanced data. With an ONNX-based model, you gain context-aware detection for **names, addresses, phone numbers, etc.**
- **Safe LLM Usage**: Many companies need to keep real customer or employee data **internal** but still want to leverage powerful external LLMs. This tool helps them do so by anonymizing data **on their end** first.
- **Flexible**: Supports `.txt`, `.docx`, `.xls(x)`, `.csv`, `.pdf`, and more. Converts text, merges tokens, and replaces them with consistent pseudonyms.
## Key Features
1. **Electron App**: Cross-platform desktop UI built on HTML/CSS/JS.
2. **Daily Limit**: **100** documents per day for the free tier (by default). You can raise or remove it if you prefer—this is open source.
3. **Context-Aware**: Relies on a local ONNX model downloaded separately (due to GitHub’s file-size constraints).
4. **Mapping** (Pro Mode): If you enable Pro, the app can produce a JSON file mapping each original entity (e.g., “John Smith”) to its anonymized token (e.g., “NAME_1”).
5. **MIT License**: Free to modify and distribute. We welcome contributions.
## Getting Started
1. **Clone or Download** this repository.
- Model available here: [https://huggingface.co/iiiorg/piiranha-v1-detect-personal-information/tree/main](https://huggingface.co/iiiorg/piiranha-v1-detect-personal-information/tree/main)
- Download the ONNX model (`.onnx` files) from our external link (not included here due to size constraints).
- Place it under `./models/protectai/lakshyakh93-deberta_finetuned_pii-onnx/` or as directed in `fileProcessor.js`.
3. **Install Dependencies**:
```bash
npm install
```
4. **Run (Dev Mode)**:
```bash
npm run dev
```
Or if you prefer:
```bash
npx electron .
```
5. **Build / Package** (macOS example):
```bash
npm run build:mac
### Basic Usage
- **Drop or Select Files**: The main UI allows you to drag-and-drop or pick multiple files/folders.
- **Output Directory**: Choose where the anonymized files should be placed.
- **Anonymize**: Click “Anonymize Files” to run.
- **Mapping (Pro)**: If you have a Pro key, the app will create an additional `-map.json` file capturing each replaced entity.
## How It Works
- **Electron**:
- **`main.js`**: Spawns the main window, handles file selection, passes tasks to `FileProcessor`.
- **`renderer.js`**: Manages the UI (index.html), user interactions, daily usage counters, and “Pro” logic.
- **`fileProcessor.js`**:
- Loads the local ONNX model (via `@xenova/transformers`).
- Identifies personal data by context (names, addresses, etc.).
- Replaces them with tokens (`NAME_1`, `PHONE_NUMBER_3`, etc.).
- If Pro, writes a JSON mapping for re-identification.
- **Local Model**:
- We rely on a context-aware token classification model. This is significantly more effective than simple RegEx for real-world PII.
## Limitations & Notes
- **Daily 100-File Limit**: By default, the free version only processes 100 documents per day. This is purely enforced in the UI. Since it’s open source, you can remove or change it as needed.
- **No Guarantee**: Even context-aware models can miss certain edge cases. Always manually review if 100% privacy is critical.
- **Cross-Platform**:
- macOS builds are tested on both M-series (ARM) and Intel.
- Windows and Linux builds are also available.
## Contributing
We use the **MIT License**, so feel free to open pull requests, modify the code, or adapt it for your needs. If you make improvements or fix bugs, please share them back!
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**Thanks for checking out the A5 PII Anonymizer.** We hope this helps you safely leverage powerful LLMs with real data while keeping personal information private.