Datasets:

erchiw
/

DPriv-Bench

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

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

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) 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.

@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}, 
}