README.md

---
base_model:
- Qwen3-4B
language:
- en
- zh
license: cc-by-nc-nd-4.0
pipeline_tag: text-generation
library_name: transformers
tags:
- privacy
- privacy-detection
- memory
- personalized-memory
- memory-system
- memory-management
- agent
- agent-memory
- information-security
- information-extraction
- edge-cloud
inference: false
---

<h1 align="center">
🛡️ MemPrivacy-4B-RL
</h1>

<p align="center">
  <div style="display: flex; justify-content: center; gap: 10px;">
    <a href="https://github.com/MemTensor/MemPrivacy">
      <img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub"/>
    </a>
    <a href="https://huggingface.co/IAAR-Shanghai/MemPrivacy-4B-RL">
      <img src="https://img.shields.io/badge/🤗%20Hugging%20Face-MemPrivacy--4B--RL-yellow" alt="Hugging Face"/>
    </a>
    <a href="https://arxiv.org/abs/2605.09530">
      <img src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv" alt="Paper"/>
    </a>
  </div>
</p>

MemPrivacy-4B-RL is a lightweight, privacy-preserving model developed from the Qwen3-4B base model and further optimized through reinforcement learning. It was introduced in the paper [MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents](https://huggingface.co/papers/2605.09530).

It is designed specifically for personalized memory management in edge-cloud agents, enabling more reliable, adaptive, and privacy-aware memory operations. This model functions as the core local extraction engine within the **MemPrivacy framework**. Instead of relying on aggressive masking that destroys task-relevant semantics, the model accurately identifies privacy-sensitive spans on edge devices, categorizes them according to a four-level privacy taxonomy, and replaces them with semantically structured, type-aware placeholders (e.g., `<Email_1>`) before transmitting data to the cloud.

---

## ✨ Key Features & Capabilities

* **High-Precision Privacy Extraction**: Achieves state-of-the-art performance in privacy information extraction, substantially surpassing strong general-purpose reasoning models like GPT-5.2 and Gemini-3.1-Pro.
* **Four-Level Privacy Taxonomy (PL1-PL4)**: Capable of identifying and classifying privacy-relevant content based on identifiability, expected harm, and operational exploitability.
* **Semantic Utility Preservation**: By decoupling privacy protection from semantic destruction, the use of typed placeholders ensures that cloud agents retain the relational and semantic cues required for effective memory formation and retrieval. 
* **Edge-Optimized Efficiency**: Designed for resource-constrained local deployment, maintaining high accuracy while significantly reducing inference latency compared to massive general-purpose LLMs.

---

## 🚀 Usage Example

The model accepts conversational text alongside basic user identifiers and extracts a structured list of privacy instances, detailing the original text, the specific privacy type, and its corresponding privacy level.

**Input:**

```text
User Name: Zhang San
Dialogue Text: Hello, my name is Zhang San, and my mobile number is 13800138000. I've been having insomnia recently, and the doctor diagnosed me with mild depression. Here is a photo of my prescription. Also, I just received a verification code 89757, please fill it in for me. By the way, I like spicy food and I speak quite directly.
```

**Output:**

```json
[
  {
    "original_text": "Zhang San",
    "privacy_type": "Real Name",
    "privacy_level": "PL2"
  },
  {
    "original_text": "13800138000",
    "privacy_type": "Phone Number",
    "privacy_level": "PL2"
  },
  {
    "original_text": "mild depression",
    "privacy_type": "Medical Health",
    "privacy_level": "PL3"
  },
  {
    "original_text": "89757",
    "privacy_type": "Verification Code",
    "privacy_level": "PL4"
  }
]
```

### 📌 Structured Privacy Extraction with vLLM

This example shows how to use vLLM to perform structured privacy information extraction from user-AI dialogues, constrained by a JSON schema.

```py
import json
from vllm import LLM, SamplingParams
from vllm.sampling_params import StructuredOutputsParams
from transformers import AutoTokenizer

privacy_schema = {
    "type": "array",
    "items": {
        "type": "object",
        "properties": {
            "original_text": {"type": "string"},
            "privacy_type": {"type": "string"},
            "privacy_level": {
                "type": "string",
                "enum": ["PL1", "PL2", "PL3", "PL4"]
            }
        },
        "required": ["original_text", "privacy_type", "privacy_level"],
        "additionalProperties": False
    }
}

model_name_or_path = "IAAR-Shanghai/MemPrivacy-4B-RL" 
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
sampling_params = SamplingParams(
    temperature=0.1, 
    top_p=0.1, 
    repetition_penalty=1.05,
    max_tokens=6144,
    structured_outputs=StructuredOutputsParams(json=privacy_schema)
) 
model = LLM(model=model_name_or_path, dtype='float16', gpu_memory_utilization=0.9) 

# Example input processing
name = 'Zhang San'
current_input = {
    "role": "user",
    "content": "Hello, my name is Zhang San, and my mobile number is 13800138000..."
}

# For full implementation details, please refer to the GitHub repository.
```

---

## 📚 Citation

```bibtex
@misc{chen2026memprivacyprivacypreservingpersonalizedmemory,
      title={MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents}, 
      author={Yining Chen and Jihao Zhao and Bo Tang and Haofen Wang and Yue Zhang and Fei Huang and Feiyu Xiong and Zhiyu Li},
      year={2026},
      eprint={2605.09530},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2605.09530}, 
}
```

## Disclaimers

This project is intended for **privacy research and evaluation**. Do **not** use it to process real user secrets without proper security controls, threat modeling, and compliance review. Always follow local laws and organizational policies.