---
library_name: peft
base_model: Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-classification
---

# Privacy Comparator

A learned model for pairwise comparison of privacy strength between messages.

---

## Model Details

### Model Description

Privacy Comparator is a learned model that compares two messages and determines which provides stronger protection of personal or sensitive information.

Given two inputs:

```
A: message  
B: message
```

the model outputs:

```
A      message A is more privacy-preserving  
B      message B is more privacy-preserving  
SAME   messages offer the same level of privacy protection
```

The model performs **relative privacy comparison** and can be applied to arbitrary message pairs, regardless of how they were generated.

It does **not**:

- detect PII  
- assign absolute privacy scores  
- generate redactions  

Instead, it learns a preference relation over messages in terms of privacy strength.

---

### Base Model

Finetuned from: Qwen/Qwen2.5-7B-Instruct

Implemented as a LoRA adapter.

---

### License

This adapter inherits the license constraints of the base model.

---

## Uses

### Intended Use

- Privacy-preserving text comparison  
- Ranking anonymization strategies  
- Evaluating relative disclosure risk  

For example, when multiple transformation strategies are applied to the same input:

```
m_i = τ(x; a_i)
```

where:

- `x` is the original message  
- `a_i` is a transformation strategy (e.g., redact, abstract, retain sensitive spans)  
- `τ` applies the chosen strategy to produce a privacy-preserving version  

Example:

Original message:

```
Lucy lives at 139 Tremont St in Boston.
```

Different strategies may produce:

```
m₁: [NAME1] lives at [ADDRESS1] in [CITY1].
m₂: A person lives at a residential address in a major city in U.S.
m₃: A person lives at [ADDRESS1] in Boston.
```

The comparator can rank such variants based on which better protects sensitive information.

For more details on the transformation framework, please refer to the associated paper.

---

### Out-of-Scope Use

This model is **not intended for**:

- PII detection  
- Safety moderation  
- Utility evaluation  
- Generating anonymized text  

It performs relative comparison only.

---

## Training Details

- LoRA rank: 8  
- Learning rate: 1e-4  
- Epochs: 2  
- Context length: 2048  
- Global batch size: 2048  

Training performed using Fireworks AI.

## Training Data

This model is fine-tuned via supervised fine-tuning (SFT) with LoRA on pairwise privacy-preference comparisons.

Training labels are generated using a teacher model (OpenAI o3) on [ShareGPT90K](https://huggingface.co/datasets/liyucheng/ShareGPT90K)-derived privacy-variant pairs.  
As described in the paper, o3 was selected based on its alignment with human ground truth under high-consensus cases.

In addition, we release a human-labeled evaluation set of 150 A/B pairs.  
Each pair is annotated by at least 5 qualified participants (52 unique participants total), with provided `consensus` labels and `consensus_ratio`.

For details on data construction, model selection, and annotation procedures, please refer to the paper.

---
## Released Dataset (Human Ground Truth)

We release a human-labeled [dataset](https://github.com/PEACH-Research-Lab/Operationalize-Data-Minimization/blob/main/human_labeled_datasets/DATASET_CARD.md) of 150 pairwise privacy-preference comparisons.

Each JSONL entry contains:
- `survey_id`, `conversation_id`, `pair_index`
- `answers`: anonymized participant votes (`participant_1`, `participant_2`, ...)
- `consensus`, `consensus_ratio`
- `message_A`, `message_B`

### Participant Privacy
All participant identifiers are anonymized. No Prolific IDs or direct participant identifiers are released.

---

## Model Outputs

The model produces structured JSON decisions:

```json
{
  "reason": "...",
  "response": "A" | "B" | "SAME"
}
```

---

## Resources

Paper: [OpenReview](https://iclr.cc/virtual/2026/poster/10007115)  
Code: [Operationalize Data Minimization](https://github.com/PEACH-Research-Lab/Operationalize-Data-Minimization)

For full details of the transformation framework and action search procedure, please refer to the paper.