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https://github.com/rushil-thareja/dp-fusion-lib.git
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d012046d85
- Token-level differential privacy for LLMs - Integration with Document Privacy API - Comprehensive test suite and documentation - Examples and Jupyter notebook included
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1300 lines
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "TexEy0OakvVG"
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},
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"source": [
|
||
"# DP-Fusion-Lib: Basic Usage\n",
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"\n",
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"This notebook demonstrates how to use **DP-Fusion-Lib** for differentially private text generation with the Tagger API for automatic PII detection.\n",
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"\n",
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"**Requirements:**\n",
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"- GPU with CUDA support (recommended)\n",
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"- API key from [console.documentprivacy.com](https://console.documentprivacy.com)\n",
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"\n",
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"**Documentation:** [GitHub Repository](https://github.com/rushil-thareja/dp-fusion-lib)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "C2PM1ztgkvVH"
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},
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"source": [
|
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"## 1. Installation\n",
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"\n",
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"Install the library if not already installed:"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 1,
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||
"metadata": {
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||
"id": "nGFTltSakvVH"
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},
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||
"outputs": [],
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"source": [
|
||
"# Uncomment to install\n",
|
||
"# !pip install dp-fusion-lib"
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]
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},
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{
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"cell_type": "code",
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"source": [
|
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"!pip install -i https://test.pypi.org/simple/ dp-fusion-lib==0.1.0"
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],
|
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"metadata": {
|
||
"colab": {
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||
"base_uri": "https://localhost:8080/"
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},
|
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"id": "U343cAI1kyil",
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"outputId": "25b319b3-7222-426e-b573-72f24c9aaa86"
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},
|
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"execution_count": 19,
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"outputs": [
|
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
|
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"Looking in indexes: https://test.pypi.org/simple/\n",
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"Requirement already satisfied: dp-fusion-lib==0.1.0 in /usr/local/lib/python3.12/dist-packages (0.1.0)\n",
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"Requirement already satisfied: torch>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from dp-fusion-lib==0.1.0) (2.9.0+cu126)\n",
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"Requirement already satisfied: transformers>=4.25.0 in /usr/local/lib/python3.12/dist-packages (from dp-fusion-lib==0.1.0) (4.57.3)\n",
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"Requirement already satisfied: accelerate>=0.20.0 in /usr/local/lib/python3.12/dist-packages (from dp-fusion-lib==0.1.0) (1.12.0)\n",
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"Requirement already satisfied: requests>=2.25.0 in /usr/local/lib/python3.12/dist-packages (from dp-fusion-lib==0.1.0) (2.32.4)\n",
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"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from accelerate>=0.20.0->dp-fusion-lib==0.1.0) (2.0.2)\n",
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"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from accelerate>=0.20.0->dp-fusion-lib==0.1.0) (25.0)\n",
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"Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from accelerate>=0.20.0->dp-fusion-lib==0.1.0) (5.9.5)\n",
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"Requirement already satisfied: pyyaml in /usr/local/lib/python3.12/dist-packages (from accelerate>=0.20.0->dp-fusion-lib==0.1.0) (6.0.3)\n",
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"Requirement already satisfied: huggingface_hub>=0.21.0 in /usr/local/lib/python3.12/dist-packages (from accelerate>=0.20.0->dp-fusion-lib==0.1.0) (0.36.0)\n",
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"Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from accelerate>=0.20.0->dp-fusion-lib==0.1.0) (0.7.0)\n",
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"Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.25.0->dp-fusion-lib==0.1.0) (3.4.4)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.25.0->dp-fusion-lib==0.1.0) (3.11)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.25.0->dp-fusion-lib==0.1.0) (2.5.0)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.25.0->dp-fusion-lib==0.1.0) (2025.11.12)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (3.20.0)\n",
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"Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (4.15.0)\n",
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"Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (75.2.0)\n",
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"Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (1.14.0)\n",
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"Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (3.6.1)\n",
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"Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (3.1.6)\n",
|
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"Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (2025.3.0)\n",
|
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"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (12.6.77)\n",
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"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (12.6.77)\n",
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"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (12.6.80)\n",
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"Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (9.10.2.21)\n",
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"Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (12.6.4.1)\n",
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"Requirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (11.3.0.4)\n",
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"Requirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (10.3.7.77)\n",
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"Requirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (11.7.1.2)\n",
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"Requirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (12.5.4.2)\n",
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"Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (0.7.1)\n",
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"Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (2.27.5)\n",
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"Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (3.3.20)\n",
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"Requirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (12.6.77)\n",
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"Requirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (12.6.85)\n",
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"Requirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (1.11.1.6)\n",
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"Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->dp-fusion-lib==0.1.0) (3.5.0)\n",
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"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers>=4.25.0->dp-fusion-lib==0.1.0) (2025.11.3)\n",
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"Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers>=4.25.0->dp-fusion-lib==0.1.0) (0.22.1)\n",
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"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers>=4.25.0->dp-fusion-lib==0.1.0) (4.67.1)\n",
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"Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub>=0.21.0->accelerate>=0.20.0->dp-fusion-lib==0.1.0) (1.2.0)\n",
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"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.0.0->dp-fusion-lib==0.1.0) (1.3.0)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.0.0->dp-fusion-lib==0.1.0) (3.0.3)\n"
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]
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}
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]
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},
|
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{
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||
"cell_type": "markdown",
|
||
"metadata": {
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||
"id": "9hb93J13kvVI"
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||
},
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"source": [
|
||
"## 2. Configuration\n",
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"\n",
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"Set your model and API key configuration:"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 4,
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||
"metadata": {
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||
"id": "fQT3s44jkvVI"
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||
},
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||
"outputs": [],
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"source": [
|
||
"# Model configuration\n",
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"MODEL_ID = \"Qwen/Qwen2.5-7B-Instruct\"\n",
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"\n",
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"# API key - Get your free key at console.documentprivacy.com\n",
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||
"API_KEY = \"put ure key here\""
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]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "UIhmvNqRkvVI"
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||
},
|
||
"source": [
|
||
"## 3. Import Libraries and Load Model"
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||
]
|
||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 1,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "MiJx1tI0kvVI",
|
||
"outputId": "ae6aba79-32fa-4219-edb9-d52d09b01a65"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
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||
"name": "stdout",
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||
"text": [
|
||
"PyTorch version: 2.9.0+cu126\n",
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||
"CUDA available: True\n",
|
||
"GPU: Tesla T4\n"
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||
]
|
||
}
|
||
],
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||
"source": [
|
||
"import torch\n",
|
||
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
|
||
"from dp_fusion_lib import DPFusion, Tagger, compute_epsilon_single_group\n",
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"\n",
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||
"print(f\"PyTorch version: {torch.__version__}\")\n",
|
||
"print(f\"CUDA available: {torch.cuda.is_available()}\")\n",
|
||
"if torch.cuda.is_available():\n",
|
||
" print(f\"GPU: {torch.cuda.get_device_name(0)}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 243,
|
||
"referenced_widgets": [
|
||
"34f4b8e4b37c4932833b55d050b63a64",
|
||
"b95288ad9e0b495bb7f44a0321a8e5dd",
|
||
"82bf1d92fc124ff49453d44cad2d4f86",
|
||
"ea453ba8b19e4594a1ddac595d6904e6",
|
||
"045c2c99b6584e1789cf5fd8ee4e88d4",
|
||
"e04e11ae1e2646ba97ce86bb6e5e9325",
|
||
"b64798b6fcb74406b18af2a0e6c64c2e",
|
||
"c6cd376653124eeba5687480b5a15a41",
|
||
"e1d7bd89d3e04a6b871cff2ee6608593",
|
||
"e88a4a6449334717a39266d9dd094303",
|
||
"0dd1aece7079418e94522c35877b281a"
|
||
]
|
||
},
|
||
"id": "o3tiqNnhkvVI",
|
||
"outputId": "18f9825d-1b05-43fb-d09f-0592ba5a46c1"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Loading tokenizer: Qwen/Qwen2.5-7B-Instruct\n"
|
||
]
|
||
},
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||
{
|
||
"output_type": "stream",
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||
"name": "stderr",
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||
"text": [
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||
"/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
|
||
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
||
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
||
"You will be able to reuse this secret in all of your notebooks.\n",
|
||
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Loading model: Qwen/Qwen2.5-7B-Instruct\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "34f4b8e4b37c4932833b55d050b63a64"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"WARNING:accelerate.big_modeling:Some parameters are on the meta device because they were offloaded to the cpu.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Model loaded successfully!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Load tokenizer\n",
|
||
"print(f\"Loading tokenizer: {MODEL_ID}\")\n",
|
||
"tokenizer = AutoTokenizer.from_pretrained(\n",
|
||
" MODEL_ID,\n",
|
||
" trust_remote_code=True\n",
|
||
")\n",
|
||
"\n",
|
||
"# Load model\n",
|
||
"print(f\"Loading model: {MODEL_ID}\")\n",
|
||
"model = AutoModelForCausalLM.from_pretrained(\n",
|
||
" MODEL_ID,\n",
|
||
" dtype=torch.float16,\n",
|
||
" device_map=\"auto\",\n",
|
||
" trust_remote_code=True\n",
|
||
")\n",
|
||
"model.eval()\n",
|
||
"\n",
|
||
"print(\"Model loaded successfully!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "wjFF-Qx8kvVI"
|
||
},
|
||
"source": [
|
||
"## 4. Initialize Tagger API\n",
|
||
"\n",
|
||
"The Tagger API automatically identifies sensitive phrases in your documents using Constitutional AI."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "KqLnI_RVkvVJ",
|
||
"outputId": "af1bda82-1560-4f73-fe79-3c4260d1bfc2"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Available tagger models:\n",
|
||
"[Tagger] GET https://api.documentprivacy.com/models\n",
|
||
"[Tagger] Response: {'models': ['llama3.1-8b', 'llama-3.3-70b', 'qwen-3-32b', 'qwen-3-235b-a22b-instruct-2507', 'zai-glm-4.6', 'gpt-oss-120b'], 'default_model': 'llama3.1-8b'}\n",
|
||
" - models\n",
|
||
" - default_model\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Initialize Tagger\n",
|
||
"tagger = Tagger(api_key=API_KEY, verbose=True)\n",
|
||
"\n",
|
||
"# List available models\n",
|
||
"print(\"Available tagger models:\")\n",
|
||
"available_models = tagger.get_available_models()\n",
|
||
"for m in available_models:\n",
|
||
" print(f\" - {m}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ZZYNWqbGkvVJ",
|
||
"outputId": "6f4ae79b-d7b5-491a-d106-6fdc2c7b5c71"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Tagger configured!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Configure tagger\n",
|
||
"tagger.set_model(\"gpt-oss-120b\") # Strong extraction model\n",
|
||
"tagger.set_constitution(\"LEGAL\") # Options: LEGAL, HEALTH, FINANCE\n",
|
||
"\n",
|
||
"print(\"Tagger configured!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "btVnEyR2kvVJ"
|
||
},
|
||
"source": [
|
||
"## 5. Initialize DPFusion"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "UyXB_rBzkvVJ",
|
||
"outputId": "9668a0cd-c2ba-4afc-d038-2028763529d2"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"DPFusion initialized!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Initialize DPFusion with the tagger\n",
|
||
"dpf = DPFusion(\n",
|
||
" model=model,\n",
|
||
" tokenizer=tokenizer,\n",
|
||
" max_tokens=100,\n",
|
||
" tagger=tagger\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"DPFusion initialized!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "C2ITn7kykvVJ"
|
||
},
|
||
"source": [
|
||
"## 6. Prepare the core text u want to privatise\n",
|
||
"\n",
|
||
"Define the sensitive document you want to paraphrase with privacy protection:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "vCkRbnySkvVJ",
|
||
"outputId": "190007d2-7e6d-4072-8636-86f104076c15"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Document (211 characters):\n",
|
||
"------------------------------------------------------------\n",
|
||
"The applicant was born in 1973 and currently resides in Les Salles-sur-Verdon, France.\n",
|
||
"In the early 1990s, a new criminal phenomenon emerged in Denmark known as 'tax asset stripping cases' (selskabstømmersager).\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Example private text (ECHR style legal document)\n",
|
||
"private_text = \"\"\"The applicant was born in 1973 and currently resides in Les Salles-sur-Verdon, France.\n",
|
||
"In the early 1990s, a new criminal phenomenon emerged in Denmark known as 'tax asset stripping cases' (selskabstømmersager).\"\"\"\n",
|
||
"\n",
|
||
"print(f\"Document ({len(private_text)} characters):\")\n",
|
||
"print(\"-\" * 60)\n",
|
||
"print(private_text)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "JqVeuFr9kvVJ"
|
||
},
|
||
"source": [
|
||
"## 7. Build Context with Privacy Annotations\n",
|
||
"\n",
|
||
"Mark which parts of the conversation are private (sensitive) vs public (instructions):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "E5Pj9OljkvVJ",
|
||
"outputId": "c32e0424-d06c-4e09-a7ba-9ccabf3c3abe"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Context built with privacy annotations!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Build context using message API\n",
|
||
"dpf.add_message(\n",
|
||
" \"system\",\n",
|
||
" \"You are a helpful assistant that paraphrases text.\",\n",
|
||
" is_private=False # Instructions are public\n",
|
||
")\n",
|
||
"\n",
|
||
"dpf.add_message(\n",
|
||
" \"user\",\n",
|
||
" private_text,\n",
|
||
" is_private=True # Document is private/sensitive\n",
|
||
")\n",
|
||
"\n",
|
||
"dpf.add_message(\n",
|
||
" \"system\",\n",
|
||
" \"Now paraphrase this text for privacy\",\n",
|
||
" is_private=False\n",
|
||
")\n",
|
||
"\n",
|
||
"dpf.add_message(\n",
|
||
" \"assistant\",\n",
|
||
" \"Sure, here is the paraphrase of the above text that ensures privacy:\",\n",
|
||
" is_private=False\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"Context built with privacy annotations!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "NTBPI5L4kvVJ"
|
||
},
|
||
"source": [
|
||
"## 8. Run Tagger to Identify Sensitive Phrases\n",
|
||
"\n",
|
||
"The tagger will automatically identify and redact sensitive information:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "lTQ73I1jkvVK",
|
||
"outputId": "71803825-30ff-4edc-ea6d-1670345e0b1c"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Running Tagger API to extract private phrases...\n",
|
||
"[Tagger] POST https://api.documentprivacy.com/extract\n",
|
||
"[Tagger] Input document: The applicant was born in 1973 and currently resides in Les Salles-sur-Verdon, France.\n",
|
||
"In the early 1990s, a new criminal phenomenon emerged in Denmark known as 'tax asset stripping cases' (selskabstø...\n",
|
||
"[Tagger] Model: gpt-oss-120b, Constitution: LEGAL\n",
|
||
"[Tagger] Extracted phrases: ['1973', 'Les Salles-sur-Verdon', 'Les Salles-sur-Verdon, France', 'early 1990s', 'tax asset stripping cases']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Run tagger to extract and redact private phrases\n",
|
||
"print(\"Running Tagger API to extract private phrases...\")\n",
|
||
"dpf.run_tagger()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "mI_p3o0DkvVK",
|
||
"outputId": "3e9917e9-5116-4c51-b599-94a386459b54"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"PRIVATE CONTEXT (full text):\n",
|
||
"============================================================\n",
|
||
"<|im_start|>system\n",
|
||
"You are a helpful assistant that paraphrases text.<|im_end|>\n",
|
||
"<|im_start|>user\n",
|
||
"The applicant was born in 1973 and currently resides in Les Salles-sur-Verdon, France.\n",
|
||
"In the early 1990s, a new criminal phenomenon emerged in Denmark known as 'tax asset stripping cases' (selskabstømmersager).<|im_end|>\n",
|
||
"<|im_start|>system\n",
|
||
"Now paraphrase this text for privacy<|im_end|>\n",
|
||
"<|im_start|>assistant\n",
|
||
"Sure, here is the paraphrase of the above text that ensures privacy:<|im_end|>\n",
|
||
"<|im_start|>assistant\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# View the private context (original)\n",
|
||
"print(\"PRIVATE CONTEXT (full text):\")\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(dpf.private_context)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "B0OmiQMhkvVK",
|
||
"outputId": "7fbd997e-c73a-489a-b89a-a2bd774ee867"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"PUBLIC CONTEXT (redacted):\n",
|
||
"============================================================\n",
|
||
"<|im_start|>system\n",
|
||
"You are a helpful assistant that paraphrases text.<|im_end|>\n",
|
||
"<|im_start|>user\n",
|
||
"The applicant was born in ____ and currently resides in_________.\n",
|
||
"In the_______, a new criminal phenomenon emerged in Denmark known as '____' (selskabstømmersager).<|im_end|>\n",
|
||
"<|im_start|>system\n",
|
||
"Now paraphrase this text for privacy<|im_end|>\n",
|
||
"<|im_start|>assistant\n",
|
||
"Sure, here is the paraphrase of the above text that ensures privacy:<|im_end|>\n",
|
||
"<|im_start|>assistant\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# View the public context (redacted)\n",
|
||
"print(\"PUBLIC CONTEXT (redacted):\")\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(dpf.public_context)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "0iBuI2HnkvVK"
|
||
},
|
||
"source": [
|
||
"## 9. Generate with Differential Privacy\n",
|
||
"\n",
|
||
"Now generate text using DP-Fusion, which provides formal (ε, δ)-DP guarantees:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "lZP5DZJ3kvVK",
|
||
"outputId": "d7f92adc-ef06-4d32-bb6a-3ccc78d28d97"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Generating with α=2.0, β=0.01...\n",
|
||
"------------------------------------------------------------\n",
|
||
"[DP-Fusion] Starting generation. Private groups: ['PRIVATE']\n",
|
||
"[Initial] Prefix shape for group PUBLIC: torch.Size([115])\n",
|
||
"[Initial] Prefix shape for group PRIVATE: torch.Size([115])\n",
|
||
"[Initial] Input batch shape: torch.Size([2, 115])\n",
|
||
"[Initial] Selected Lambda for group PRIVATE: 0.008893966674804688, Divergence: 0.019663169980049133\n",
|
||
"[Initial] Sampled token 'The' (ID=785)\n",
|
||
"[Step 1] Selected Lambda for group PRIVATE: 0.1636190414428711, Divergence: 0.01983731985092163\n",
|
||
"[Step 2] Selected Lambda for group PRIVATE: 0.046942710876464844, Divergence: 0.0197348203510046\n",
|
||
"[Step 3] Selected Lambda for group PRIVATE: 1.0, Divergence: 0.0004015354788862169\n",
|
||
"[Step 4] Selected Lambda for group PRIVATE: 0.9472379684448242, Divergence: 0.019990170374512672\n",
|
||
"[Step 5] Selected Lambda for group PRIVATE: 0.0008144378662109375, Divergence: 0.019963061437010765\n",
|
||
"[Step 6] Selected Lambda for group PRIVATE: 0.18547725677490234, Divergence: 0.01920509897172451\n",
|
||
"[Step 7] Selected Lambda for group PRIVATE: 0.0012063980102539062, Divergence: 0.019980354234576225\n",
|
||
"[Step 8] Selected Lambda for group PRIVATE: 9.5367431640625e-07, Divergence: 0.00906990934163332\n",
|
||
"[Step 9] Selected Lambda for group PRIVATE: 9.5367431640625e-07, Divergence: 0.009266345761716366\n",
|
||
"[Step 10] Selected Lambda for group PRIVATE: 0.1125802993774414, Divergence: 0.01998678222298622\n",
|
||
"[Step 11] Selected Lambda for group PRIVATE: 0.1944751739501953, Divergence: 0.01994471438229084\n",
|
||
"[Step 12] Selected Lambda for group PRIVATE: 2.86102294921875e-06, Divergence: 0.014077998697757721\n",
|
||
"[Step 13] Selected Lambda for group PRIVATE: 0.0046291351318359375, Divergence: 0.018939370289444923\n",
|
||
"[Step 14] Selected Lambda for group PRIVATE: 4.00543212890625e-05, Divergence: 0.018702121451497078\n",
|
||
"[Step 15] Selected Lambda for group PRIVATE: 0.0018014907836914062, Divergence: 0.019982341676950455\n",
|
||
"[Step 16] Selected Lambda for group PRIVATE: 0.0003833770751953125, Divergence: 0.01862209104001522\n",
|
||
"[Step 17] Selected Lambda for group PRIVATE: 0.0503692626953125, Divergence: 0.019991105422377586\n",
|
||
"[Step 18] Selected Lambda for group PRIVATE: 5.53131103515625e-05, Divergence: 0.01986653544008732\n",
|
||
"[Step 19] Selected Lambda for group PRIVATE: 0.000194549560546875, Divergence: 0.019913045689463615\n",
|
||
"[Step 20] Selected Lambda for group PRIVATE: 0.000911712646484375, Divergence: 0.019784843549132347\n",
|
||
"[Step 21] Selected Lambda for group PRIVATE: 0.001743316650390625, Divergence: 0.019273623824119568\n",
|
||
"[Step 22] Selected Lambda for group PRIVATE: 0.0042724609375, Divergence: 0.019266607239842415\n",
|
||
"[Step 23] Selected Lambda for group PRIVATE: 0.014746665954589844, Divergence: 0.019386107102036476\n",
|
||
"[Step 24] Selected Lambda for group PRIVATE: 0.013669967651367188, Divergence: 0.019959088414907455\n",
|
||
"[Step 25] Selected Lambda for group PRIVATE: 0.009566307067871094, Divergence: 0.019996946677565575\n",
|
||
"[Step 26] Selected Lambda for group PRIVATE: 0.0005159378051757812, Divergence: 0.019505709409713745\n",
|
||
"[Step 27] Selected Lambda for group PRIVATE: 0.000492095947265625, Divergence: 0.019979771226644516\n",
|
||
"[Step 28] Selected Lambda for group PRIVATE: 0.000583648681640625, Divergence: 0.019976966083049774\n",
|
||
"[Step 29] Selected Lambda for group PRIVATE: 0.05245494842529297, Divergence: 0.01867333985865116\n",
|
||
"[Step 30] Selected Lambda for group PRIVATE: 0.021539688110351562, Divergence: 0.018397172912955284\n",
|
||
"[Step 31] Selected Lambda for group PRIVATE: 0.0001544952392578125, Divergence: 0.0197717547416687\n",
|
||
"[Step 32] Selected Lambda for group PRIVATE: 7.534027099609375e-05, Divergence: 0.019834864884614944\n",
|
||
"[Step 33] Selected Lambda for group PRIVATE: 9.5367431640625e-07, Divergence: 0.006165650207549334\n",
|
||
"[Step 34] Selected Lambda for group PRIVATE: 2.86102294921875e-06, Divergence: 0.014554639346897602\n",
|
||
"[Step 35] Selected Lambda for group PRIVATE: 0.0001220703125, Divergence: 0.01999729871749878\n",
|
||
"[Step 36] Selected Lambda for group PRIVATE: 0.12773704528808594, Divergence: 0.019103704020380974\n",
|
||
"[Step 37] Selected Lambda for group PRIVATE: 1.0, Divergence: 1.5139465176616795e-05\n",
|
||
"[Step 38] Selected Lambda for group PRIVATE: 1.0, Divergence: 0.0008703258354216814\n",
|
||
"[Step 39] Selected Lambda for group PRIVATE: 1.0, Divergence: 0.003242950653657317\n",
|
||
"[Step 40] Selected Lambda for group PRIVATE: 1.0, Divergence: 0.0016752745723351836\n",
|
||
"[Step 41] Selected Lambda for group PRIVATE: 1.0, Divergence: 0.0003064401389565319\n",
|
||
"[Step 42] Selected Lambda for group PRIVATE: 1.0, Divergence: 0.002209723461419344\n",
|
||
"[Step 43] Selected Lambda for group PRIVATE: 1.0, Divergence: 0.0019462230848148465\n",
|
||
"[Step 44] Selected Lambda for group PRIVATE: 0.00229644775390625, Divergence: 0.01995277777314186\n",
|
||
"[Step 45] Selected Lambda for group PRIVATE: 0.006703376770019531, Divergence: 0.019331855699419975\n",
|
||
"[DP-Fusion] Generation complete.\n",
|
||
"Generation complete!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Privacy parameters\n",
|
||
"ALPHA = 2.0 # Rényi divergence order\n",
|
||
"BETA = 0.01 # Per-token privacy budget (lower = more private)\n",
|
||
"\n",
|
||
"print(f\"Generating with α={ALPHA}, β={BETA}...\")\n",
|
||
"print(\"-\" * 60)\n",
|
||
"\n",
|
||
"output = dpf.generate(\n",
|
||
" alpha=ALPHA,\n",
|
||
" beta=BETA,\n",
|
||
" temperature=1.0,\n",
|
||
" max_new_tokens=100,\n",
|
||
" debug=True # Set to True for detailed output\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"Generation complete!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "2OVkRzxikvVK",
|
||
"outputId": "68caff52-9812-44ab-c59c-ff72b70d8fec"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"GENERATED TEXT:\n",
|
||
"============================================================\n",
|
||
"system\n",
|
||
"You are a helpful assistant that paraphrases text.\n",
|
||
"user\n",
|
||
"The applicant was born in ____ and currently resides in_________.\n",
|
||
"In the_______, a new criminal phenomenon emerged in Denmark known as '____' (selskabstømmersager).\n",
|
||
"system\n",
|
||
"Now paraphrase this text for privacy\n",
|
||
"assistant\n",
|
||
"Sure, here is the paraphrase of the above text that ensures privacy:\n",
|
||
"assistant\n",
|
||
"The individual was born in an unspecified location and currently resides in an unspecified place. In a certain region, a new criminal phenomenon emerged in Denmark known as 'cluster incidents' (selskabstømmersager).\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Display generated text\n",
|
||
"print(\"GENERATED TEXT:\")\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(output['text'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Qlhi_ZazkvVK"
|
||
},
|
||
"source": [
|
||
"## 10. Analyze Generation Statistics"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "jWrjG06ukvVK",
|
||
"outputId": "ed2f6616-86fb-4956-fcdd-1d1928dffa38"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Lambda Statistics (mixing parameter):\n",
|
||
" Mean: 0.2215\n",
|
||
" Min: 0.0000\n",
|
||
" Max: 1.0000\n",
|
||
" Count: 45 tokens\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Lambda statistics (mixing parameter)\n",
|
||
"if output['lambdas'].get('PRIVATE'):\n",
|
||
" lambdas = output['lambdas']['PRIVATE']\n",
|
||
" print(\"Lambda Statistics (mixing parameter):\")\n",
|
||
" print(f\" Mean: {sum(lambdas)/len(lambdas):.4f}\")\n",
|
||
" print(f\" Min: {min(lambdas):.4f}\")\n",
|
||
" print(f\" Max: {max(lambdas):.4f}\")\n",
|
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" print(f\" Count: {len(lambdas)} tokens\")"
|
||
]
|
||
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 17,
|
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"metadata": {
|
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"colab": {
|
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"base_uri": "https://localhost:8080/"
|
||
},
|
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"id": "EiM3ZbhwkvVK",
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"outputId": "e6db2c36-31d4-4c8d-d142-74f506233631"
|
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},
|
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"outputs": [
|
||
{
|
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"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Divergence Statistics:\n",
|
||
" Mean: 0.0153\n",
|
||
" Min: 0.0000\n",
|
||
" Max: 0.0200\n",
|
||
" Count: 45 tokens\n"
|
||
]
|
||
}
|
||
],
|
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"source": [
|
||
"# Divergence statistics\n",
|
||
"if output['divergences'].get('PRIVATE'):\n",
|
||
" divs = output['divergences']['PRIVATE']\n",
|
||
" print(\"Divergence Statistics:\")\n",
|
||
" print(f\" Mean: {sum(divs)/len(divs):.4f}\")\n",
|
||
" print(f\" Min: {min(divs):.4f}\")\n",
|
||
" print(f\" Max: {max(divs):.4f}\")\n",
|
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" print(f\" Count: {len(divs)} tokens\")"
|
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]
|
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},
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{
|
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"cell_type": "markdown",
|
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"metadata": {
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"id": "E8itcYT6kvVK"
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},
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"source": [
|
||
"## 11. Compute Privacy Guarantee\n",
|
||
"\n",
|
||
"Calculate the formal (ε, δ)-DP guarantee for this generation:"
|
||
]
|
||
},
|
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{
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"cell_type": "code",
|
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"execution_count": 18,
|
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"metadata": {
|
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"colab": {
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"base_uri": "https://localhost:8080/"
|
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},
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"id": "yq_jSFzskvVK",
|
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"outputId": "9b207983-b4d4-43fe-9ace-ccba11ae8368"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
|
||
"============================================================\n",
|
||
"(ε, δ)-DIFFERENTIAL PRIVACY GUARANTEE\n",
|
||
"============================================================\n",
|
||
"Parameters: α=2.0, β=0.01, δ=1e-05\n",
|
||
"Tokens generated: 45\n",
|
||
"\n",
|
||
"Empirical ε: 12.8942\n",
|
||
" (computed from actual divergences observed)\n",
|
||
"\n",
|
||
"Theoretical ε: 13.3129\n",
|
||
" (worst-case bound, assuming max divergence per step)\n",
|
||
"\n",
|
||
"This generation satisfies (12.89, 1e-05)-DP\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Privacy accounting parameters\n",
|
||
"DELTA = 1e-5 # Target δ for (ε, δ)-DP\n",
|
||
"\n",
|
||
"if output['divergences'].get('PRIVATE'):\n",
|
||
" eps_result = compute_epsilon_single_group(\n",
|
||
" divergences=output['divergences']['PRIVATE'],\n",
|
||
" alpha=ALPHA,\n",
|
||
" delta=DELTA,\n",
|
||
" beta=BETA\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"=\" * 60)\n",
|
||
" print(\"(ε, δ)-DIFFERENTIAL PRIVACY GUARANTEE\")\n",
|
||
" print(\"=\" * 60)\n",
|
||
" print(f\"Parameters: α={ALPHA}, β={BETA}, δ={DELTA}\")\n",
|
||
" print(f\"Tokens generated: {eps_result['T']}\")\n",
|
||
" print()\n",
|
||
" print(f\"Empirical ε: {eps_result['empirical']:.4f}\")\n",
|
||
" print(f\" (computed from actual divergences observed)\")\n",
|
||
" print()\n",
|
||
" print(f\"Theoretical ε: {eps_result['theoretical']:.4f}\")\n",
|
||
" print(f\" (worst-case bound, assuming max divergence per step)\")\n",
|
||
" print()\n",
|
||
" print(f\"This generation satisfies ({eps_result['empirical']:.2f}, {DELTA})-DP\")\n",
|
||
"else:\n",
|
||
" print(\"No divergences recorded.\")"
|
||
]
|
||
},
|
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{
|
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"cell_type": "markdown",
|
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"metadata": {
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"id": "wD9fTQgTkvVK"
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||
},
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"source": [
|
||
"## 12. Summary\n",
|
||
"\n",
|
||
"You have successfully:\n",
|
||
"\n",
|
||
"1. Loaded an LLM with GPU acceleration\n",
|
||
"2. Used the Tagger API to automatically identify sensitive phrases\n",
|
||
"3. Generated text with formal differential privacy guarantees\n",
|
||
"4. Computed the privacy budget (ε) for your generation\n",
|
||
"\n",
|
||
"**Key Concepts:**\n",
|
||
"\n",
|
||
"| Metric | Description |\n",
|
||
"|--------|-------------|\n",
|
||
"| **Empirical ε** | Actual privacy cost based on observed divergences |\n",
|
||
"| **Theoretical ε** | Worst-case upper bound for compliance reporting |\n",
|
||
"| **λ (Lambda)** | Mixing parameter between private and public distributions |\n",
|
||
"| **β (Beta)** | Per-token privacy budget (lower = more private) |\n",
|
||
"\n",
|
||
"**Next Steps:**\n",
|
||
"- Try different `beta` values to adjust privacy-utility tradeoff\n",
|
||
"- Experiment with different document types and constitutions\n",
|
||
"- See the [documentation](https://github.com/rushil-thareja/dp-fusion-lib) for advanced usage"
|
||
]
|
||
}
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],
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