You Only Anonymize What Is Not Intent-Relevant: Suppressing Non-Intent Privacy Evidence

📄 arXiv   |   🛡️ Intent-Aware Privacy Protection   |   🤖 Large Language Models

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🔍 Overview

IntentAnony is a pragmatic intent-conditioned text anonymization framework built on large language models (LLMs).
It protects user privacy under inference-based threat models while preserving communicative intent and textual utility. Unlike surface-level masking or generic rewriting, IntentAnony selectively suppresses non-intent privacy evidence, ensuring that only information irrelevant to the user’s communicative intent is anonymized.

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✨ Key Features

• 🎯 Intent-aware anonymization rather than blanket masking
• 🛡️ Defense against attribute inference and profiling attacks
• 📊 Integrated privacy–utility evaluation (automatic + human)
• 🔄 Supports multiple anonymization strategies and threat settings

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📁 Project Structure

IntentAnony_Updated/
├── anonymized/                 # Core anonymization module
│   ├── anonymizers/            # Anonymizer implementations
│   ├── run_workflow.py         # End-to-end anonymization workflow
│   └── eval_workflow.py        # Privacy & utility evaluation workflow
├── configs/                    # Configuration definitions
│   └── config.py               # Configuration class
├── privacy_configs/            # Example privacy task configurations
├── prompt_kits/                # Prompt management and policies
│   ├── prompts/                # Prompt templates
│   └── policy_manager.py       # Policy manager
├── llm_tools/                  # LLM provider wrappers
│   ├── openai_tool.py
│   └── async_openai_tool.py
├── pu_eval/                    # Privacy & utility evaluation
│   ├── eval_privacy.py
│   ├── eval_utility.py
│   └── async_eval_utility.py
├── infer_attack/               # Inference attack implementations
├── utils/                      # Shared utility functions
├── dataset/                    # Datasets
├── main.py                     # Main entry point
└── requirements.txt            # Dependencies

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🖥️ System Requirements

• Python ≥ 3.8
• MongoDB (optional, for dataset storage and experiment logging)
• Sufficient API quotas for supported LLM providers (OpenAI, DeepSeek, Google, GLM, etc.)

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⚙️ Installation

1️⃣ Clone the repository

git clone <repository-url>
cd IntentAnony

2️⃣ Create a virtual environment (recommended)

python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Download NLTK resources (for BLEU computation)

import nltk
nltk.download('punkt')

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🔑 API Key Configuration

Create or edit llm_tools/keys.json:

{
  "openai": "sk-your-openai-api-key",
  "deepseek": "sk-your-deepseek-api-key",
  "google": "your-gemini-api-key",
  "glm": "sk-your-glm-api-key"
}

Note

• Only include providers you intend to use
• Keys are automatically loaded when initializing LLM tools

MongoDB (Optional)

export MONGODB_HOST="localhost"
export MONGODB_PORT="27017"

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🚀 Quick Start

Run a complete anonymization → inference → evaluation pipeline:

python main.py \
  --config_path ./privacy_configs/personal_reddit/synthetic_glm_ad_piec.yaml \
  --new

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🧩 Configuration

Configuration files are written in YAML and include:

output_dir: "results"
seed: 10
task: "ANONYMIZED"
dataset_name: "personal_reddit"
collection_name: "personal_reddit"

task_config:
  profile_path: "dataset/..."
  outpath: "results/..."
  anonymizer:
    anon_type: "llm"
    target_mode: "single"
  anon_model:
    name: "gemini-3-pro-preview"
    provider: "google"
    prompt_policy_version: "7.0"
  inference_model:
    name: "deepseek-reasoner"
    provider: "deepseek"
  utility_model:
    name: "deepseek-chat"
    provider: "deepseek"

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🧠 Main Modules

1️⃣ Anonymization (anonymized/)

• IntentAnonymizer – intent-aware selective anonymization
• PIECAnonymizer – privacy inference evidence chain suppression
• AzureAnonymizer – Azure text analytics anonymizer

2️⃣ Evaluation (pu_eval/)

• Privacy Evaluation – inference success & protection rate
• Utility Evaluation – BLEU, ROUGE, LLM Judge
• Attack Evaluation – adversarial inference success rate

3️⃣ LLM Tools (llm_tools/)

Supported providers include:

• OpenAI (GPT series)
• DeepSeek
• Google (Gemini)
• GLM
• Claude
• Custom providers

4️⃣ Prompt Management (prompt_kits/)

• Structured prompt organization
• Multi-language support
• Prompt versioning and policy control

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📊 Evaluation Metrics

Privacy Metrics

• Inference Accuracy
• Privacy Protection Rate

Utility Metrics

• BLEU
• ROUGE-1 / ROUGE-L / ROUGE-Lsum
• LLM Judge (readability, semantic preservation, hallucination)

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🧪 Usage Examples

Example 1: End-to-End Anonymization

from anonymized.run_workflow import run_anon_infer_eval
from utils.initialization import read_config_from_yaml
import asyncio

cfg = read_config_from_yaml("configs/my_config.yaml")
asyncio.run(run_anon_infer_eval(cfg, {}))

Example 2: Batch Utility Evaluation

from anonymized.run import batch_evaluate_utility
from llm_tools.async_openai_tool import create_async_any_tool
from prompt_kits.prompt_manager_final import get_manager
from utils.mongo_utils import MongoDBConnector
import asyncio

prompt_manager = get_manager(default_category="eval_utility")
llm_model = create_async_any_tool(model="gpt-5", provider="openai")

mongo = MongoDBConnector()
mongo.connect()

profiles = mongo.read_data("personal_reddit", query={...})
stats = asyncio.run(batch_evaluate_utility(
    profiles=profiles,
    prompt_manager=prompt_manager,
    llm_model=llm_model,
    mongo=mongo
))

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⚠️ Notes

1. Ensure all required API keys are correctly set
2. MongoDB must be running if enabled
3. Input data should follow the expected JSONL format

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🙏 Acknowledgements

We thank the authors of
LLM-Anonymization
for releasing their code and inspiring this work.

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📌 Citation

If you use this code, please consider citing our work:

@article{intentanony2026,
  title   = {You Only Anonymize What Is Not Intent-Relevant: Suppressing Non-Intent Privacy Evidence},
  author  = {Shen, Weihao and Xu, Yaxin and Li, Shuang and Chen, Wei and Lan, Yuqin and Yuan, Meng and Zhuang, Fuzhen},
  journal = {arXiv preprint arXiv:2601.04265},
  year    = {2026}
}