Datasetpublic

shichenghu/personal-info-unlearning

Synthetic Personal Information Unlearning Dataset Dataset Description This dataset is designed for research on large language model (LLM) unlearning in controlled synthetic personal-information settings. It contains synthetic profiles and question-answer data for four personal attributes: Year of birth Blood type Postcode Social insurance number The benchmark provides three forget-set sizes: N = 5, 20, 40. All personal-profile data are synthetically generated… See the full description on the dataset page: https://huggingface.co/datasets/shichenghu/personal-info-unlearning.

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Dataset Card

Synthetic Personal Information Unlearning Dataset

Dataset Description

This dataset is designed for research on large language model (LLM) unlearning in controlled synthetic personal-information settings.

It contains synthetic profiles and question-answer data for four personal attributes:

—Year of birth
—Blood type
—Postcode
—Social insurance number

The benchmark provides three forget-set sizes: N = 5, 20, 40.

All personal-profile data are synthetically generated for studying memorization, unlearning, information recovery, and privacy auditing.

Dataset Configurations

The dataset contains 12 configurations:

Configuration	N	Target Attribute
`n5_year_of_birth`	5	Year of Birth
`n5_blood_type`	5	Blood Type
`n5_postcode`	5	Postcode
`n5_social_insurance_number`	5	Social Insurance Number
`n20_year_of_birth`	20	Year of Birth
`n20_blood_type`	20	Blood Type
`n20_postcode`	20	Postcode
`n20_social_insurance_number`	20	Social Insurance Number
`n40_year_of_birth`	40	Year of Birth
`n40_blood_type`	40	Blood Type
`n40_postcode`	40	Postcode
`n40_social_insurance_number`	40	Social Insurance Number

Example:

python

from datasets import load_dataset

dataset = load_dataset(
    "<USERNAME>/<DATASET_NAME>",
    "n20_year_of_birth"
)

Splits

Each configuration contains:

Split	Description
`forget`	Data targeted for unlearning.
`retain`	Data retained during unlearning.
`remain`	Remaining data outside the forget and retain sets.
`retain_same_attr`	Attribute-matched retain subset.
`remain_same_attr`	Attribute-matched remaining subset.
`retain_same_fn_attr`	Specialized matched retain subset.
`remain_same_fn_attr`	Specialized matched remaining subset.
`test_forget`	Test data corresponding to the forget set.
`test_retain`	Test data corresponding to the retain set.
`test_remain`	Test data corresponding to the remaining set.
`test_retain_same_attr`	Attribute-matched retain test subset.
`test_remain_same_attr`	Attribute-matched remaining test subset.
`test_retain_same_fn_attr`	Specialized matched retain test subset.
`test_remain_same_fn_attr`	Specialized matched remaining test subset.

The construction of matched subsets is provided in the accompanying codebase.

Base Data

Shared files are stored under data/base/.

File	Description
`profiles.json`	Synthetic profiles.
`training_dataset.json`	Main training dataset.
`training_testset.json`	Training test set.
`validation_dataset.json`	Validation dataset.
`validation_testset.json`	Validation test set.
`common_knowledge_questions.json`	Common-knowledge evaluation data.
`real_world_dataset.json`	Auxiliary real-world evaluation data.
`idk.jsonl`	Auxiliary "I don't know" responses.

Repository Structure

text

data/
├── base/
├── n5/
│   ├── year_of_birth/
│   ├── blood_type/
│   ├── postcode/
│   └── social_insurance_number/
├── n20/
│   ├── year_of_birth/
│   ├── blood_type/
│   ├── postcode/
│   └── social_insurance_number/
└── n40/
    ├── year_of_birth/
    ├── blood_type/
    ├── postcode/
    └── social_insurance_number/

Usage

python

from datasets import load_dataset

dataset = load_dataset(
    "<USERNAME>/<DATASET_NAME>",
    "n20_postcode"
)

print(dataset)
print(dataset["forget"][0])

Configuration names follow:

text

n{5,20,40}_{year_of_birth,blood_type,postcode,social_insurance_number}

Intended Use

This dataset is intended for research on:

—LLM unlearning
—memorization and knowledge retention
—privacy auditing
—recovery attacks
—forget quality and model utility evaluation

Synthetic Data Notice

The personal-profile data are synthetically generated and do not represent real individuals.

Auxiliary real-world and common-knowledge evaluation files may contain publicly available factual information.

Limitations

This benchmark evaluates controlled factual memorization and unlearning. Results should not be interpreted as formal privacy guarantees or proof of effective deletion in arbitrary real-world settings.