README.md

---
license: apache-2.0
github: https://github.com/erchiw/DPriv-Bench

configs:
  - config_name: cate_1_ExpoMech_pureDP
    data_files:
      - split: test
        path: "cate_1_ExpoMech_pureDP/test-*.parquet"

  - config_name: cate_1_Gaussian_GDP
    data_files:
      - split: test
        path: "cate_1_Gaussian_GDP/test-*.parquet"

  - config_name: cate_1_Gaussian_zCDP
    data_files:
      - split: test
        path: "cate_1_Gaussian_zCDP/test-*.parquet"

  - config_name: cate_1_LaplaceRNM_pureDP
    data_files:
      - split: test
        path: "cate_1_LaplaceRNM_pureDP/test-*.parquet"

  - config_name: cate_1_Laplace_pureDP
    data_files:
      - split: test
        path: "cate_1_Laplace_pureDP/test-*.parquet"

  - config_name: cate_1_PF_pureDP
    data_files:
      - split: test
        path: "cate_1_PF_pureDP/test-*.parquet"

  - config_name: cate_1_function_bank
    data_files:
      - split: test
        path: "cate_1_function_bank/test-*.parquet"

  - config_name: cate_2
    data_files:
      - split: test
        path: "cate_2/test-*.parquet"
---

# DPrivBench: Benchmarking LLMs’ Reasoning for Differential Privacy

DPrivBench is a benchmark for evaluating whether language models can correctly reason about and verify claimed differential privacy (DP) guarantees from natural-language/LaTeX-format problem statements.

This release contains evaluation data from **seven benchmark configs**, along with **one auxiliary function bank**:
- **Category 1:** 6 fundamental mechanism tracks, each with 98 questions.
- **Category 2:** 125 more advanced algorithm-level DP questions derived from literatures.

The data files are stored as `<config_name>/test-*.parquet`.

## Example Usage

```python
from datasets import load_dataset

repo = "erchiw/DPrivBench"
# Category 1
ds = load_dataset(repo, "cate_1_Laplace_pureDP", split="test")

# Category 2
ds = load_dataset(repo, "cate_2", split="test")
```

Evaluation code can be found in the [GitHub Repository](https://github.com/erchiw/DPriv-Bench)


## Dataset Structure

| Config | Description |
| --- | --- |
| `cate_1_Laplace_pureDP` | Laplace mechanism under pure DP |
| `cate_1_Gaussian_GDP` | Gaussian mechanism under GDP |
| `cate_1_Gaussian_zCDP` | Gaussian mechanism under zCDP |
| `cate_1_ExpoMech_pureDP` | Exponential mechanism under pure DP |
| `cate_1_LaplaceRNM_pureDP` | Report Noisy Max with Laplace noise |
| `cate_1_PF_pureDP` | Permute-and-Flip under pure DP |
| `cate_1_function_bank` | Function bank for category 1 questions |
| `cate_2` | algorithm-level DP questions |

## Task Format

Each example is a **yes/no verification problem** asking whether a mechanism or algorithm satisfies a claimed privacy guarantee under the stated assumptions.

### Label formats
For all benchmark configs, the `label` field is an integer:
- `1` = yes
- `0` = no


## Data Fields

### Category 1 (`cate_1_*`)

| Field | Description |
| --- | --- |
| `question_id` | Unique identifier for the question instance. |
| `question` | Text of the yes/no verification question. |
| `label` | Ground-truth label: `1` for yes, `0` for no. |
| `function_id` | Identifier of the query function in the function bank. |
| `function` | Mathematical definition of the function used in the question. |
| `function_sens` | L1 sensitivity of the function under a replace-one neighboring relation, assuming inputs in `[0,1]`. |

### Category 2 (`cate_2`)

| Field | Description |
| --- | --- |
| `question_id` | Unique identifier for the question instance. |
| `question_tex` | Full question statement in LaTeX format. |
| `label` | Ground-truth label: `1` for yes, `0` for no. |
| `citation` | Bibliographic citation(s) for the relevant paper(s). Multiple entries may be separated by `;`. |
| `negative_mode` | Construction type of the example. `"atom"` denotes a base positive/negative question; other values indicate how a negative or counterexample-style question was derived. |
| `pdf_link` | URL or pointer to the referenced source document(s). |
| `publish_year` | Publication year of the primary references. |
| `related_question` | `question_id` of the related base question, when applicable. Missing values may appear as `NaN`. |
| `section_number` | Section or location in the source where the relevant claim appears. |
| `subject` | Coarse-grained subject area. |
| `topic` | Fine-grained topic label within the subject. |
| `comments` | Short proof sketch or rationale explaining the correctness of the label. |

***Please refer to the ([rendered pdf](https://github.com/erchiw/DPriv-Bench/blob/main/cate_2_metadata.pdf)) for a reader-friendly version.***


## Notes

- This release is **test-only** and is intended for evaluation rather than training. 
- The `cate_1_function_bank` config is auxiliary and contains the function bank used to construct the Category 1 questions. For Category 1, we assume input data in $[0,1]$ and adopt the replace-one neighboring relation.
- In Category 2, the neighboring relation and input data range are specified case-by-case in each question.


## Citation

If you use this dataset, please cite the DPrivBench paper.

```bibtex
@misc{dprivbenchauthors,
  title={DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy},
  author={Erchi Wang and Pengrun Huang and Eli Chien and Om Thakkar and Kamalika Chaudhuri and Yu-Xiang Wang and Ruihan Wu},
  year={2026},
  eprint={2604.15851},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2604.15851}, 
}
```