t22000t/anonymization-before-after

Anonymization Before/After

A small paired tabular dataset showing the same records before and after a 10-step anonymization pipeline. Useful as a teaching fixture for privacy courses, a benchmark for anonymization toolkits, and a sanity-check input for red-team / membership-inference experiments.

Important: the PII in sample_raw.csv is entirely synthetic. Names follow the pattern Person_001, emails are person_001@example.com, phone numbers are 555-00XX, and "national IDs" are random 9-digit strings. No real individual is represented. The dataset is published precisely so researchers can demonstrate privacy techniques on PII-shaped data without handling real PII.

At a glance

	raw	anonymized
Rows	500	339
Columns	16	10
Direct identifiers	✅ present (4)	❌ dropped
Fingerprint columns	✅ present (2)	❌ dropped
Quasi-identifiers	exact values	generalised (bands / top-N / "Other")
Numeric fields	exact values	multiplicative noise injected
Dates	exact dates	HMAC-perturbed ±7 days
k-anonymity (over age/region/gender)	k=1 in many groups	k≥5 (rest suppressed)

The 161 missing rows in anonymized were suppressed during k-anonymity enforcement: every combination of (age band, region, gender) appears at least 5 times in the released data.

What changed, column by column

Raw column	Action	Anonymized column
full_name	dropped (direct ID)	-
email	dropped (direct ID)	-
phone_number	dropped (direct ID)	-
national_id	dropped (direct ID)	-
source_system_code	dropped (fingerprint)	-
internal_batch_id	dropped (fingerprint)	-
age (int)	banded into 10-year ranges	age (e.g. "50 - 60")
region (str)	rare categories collapsed to "Other" (top-6 kept)	region
gender (str)	passthrough	gender
marital_status (str)	passthrough	marital_status
income (float)	±5% multiplicative noise, rounded to 2dp	income
balance (float)	±3% multiplicative noise, rounded to 2dp	balance
credit_score (int)	passthrough	credit_score
transaction_amount (float)	passthrough	transaction_amount
created_date (date)	HMAC-deterministic ±7-day offset	created_date
last_login_date (date)	HMAC-deterministic ±7-day offset	last_login_date

After all transforms, columns in the anonymized split are sorted alphabetically and rows are deterministically shuffled (seed=42).

How to use

from datasets import load_dataset

raw  = load_dataset("t22000t/anonymization-before-after", "raw",        split="train")
anon = load_dataset("t22000t/anonymization-before-after", "anonymized", split="train")

print(raw[0])
# {'full_name': 'Person_001', 'email': 'person_001@example.com', ...,
#  'age': 51, 'region': 'Northeast', 'income': 40617.24, ...}

print(anon[0])
# {'age': '50 - 60', 'region': 'Other', 'income': 100899.98, ...}

Or plain pandas:

import pandas as pd
raw  = pd.read_csv("hf://datasets/t22000t/anonymization-before-after/sample_raw.csv")
anon = pd.read_csv("hf://datasets/t22000t/anonymization-before-after/sample_anonymized.csv")

Suggested uses

1. Teaching privacy concepts. The before/after diff makes k-anonymity, QI generalisation, and direct-ID suppression concrete in a single side-by-side view.
2. Benchmarking anonymization toolkits. Re-run a different tool on raw and compare its output to anonymized (or to your own adversarial baseline).
3. Red-team / membership-inference fixtures. Train an MIA classifier to separate raw from anonymized; the AUC quantifies how much identifying signal survived the pipeline.
4. Sanity-checking a synthetic-data generator. Use raw as the target distribution and compare against the generator's output on the same schema.

How it was produced

sample_raw.csv is generated by scripts/create_sample_data.py in the data-anonymization-toolkit repo (seed=42, deterministic).

sample_anonymized.csv is the output of running the toolkit's full 10-step pipeline on raw with the config config/example_simple_anonymization.yaml (profile ml, k_target=5).

Reproduce locally:

git clone https://github.com/timothy22000/data-anonymization-toolkit
cd data-anonymization-toolkit
pip install -e .

python scripts/create_sample_data.py
python scripts/anonymize_data.py \
    --input data/sample.csv \
    --output data/anonymized.csv \
    --config config/example_simple_anonymization.yaml

Limitations and caveats

• Small. 500 rows is enough to demonstrate the pipeline behaviour but not enough for serious statistical evaluation of a generator.
• Synthetic PII. Names, emails, IDs are templated, not realistic. Adversarial fingerprinting attacks that exploit real-world string distributions (e.g. uncommon surnames) won't find anything interesting here. Use a realistic synthetic-PII library (Faker, mimesis) if that matters for your research.
• One profile, one config. Only the ml profile (k=5) is provided. The public profile (k=20, stronger generalisation) would suppress many more rows.
• The "Other" category dominates region. Because the toy generator uses 8 regions but top-N keeps only 6, two regions get collapsed to "Other" in the anonymized split. Real-world QI generalisation is usually more nuanced.

Personal and sensitive information

None. All PII-shaped fields are deterministically generated from row indices. No real individual's data was used to create this dataset.

Citation

If you use this dataset in a paper or tutorial, please cite the toolkit it came from:

@software{data_anonymization_toolkit,
  author = {Mun, Timothy},
  title  = {data-anonymization-toolkit: Config-driven anonymization,
            synthetic data, and red-team validation for tabular data},
  url    = {https://github.com/timothy22000/data-anonymization-toolkit},
  year   = {2026}
}

License

MIT - see the parent toolkit repo for the full license text.

Related

• 🔒 Privacy Lab Space - interactive anonymization + red-team demo (uses this dataset as the bundled sample)
• 🛡️ Synthetic Data Privacy Audit Space - upload real + synthetic CSVs, get a privacy verdict
• 📦 data-anonymization-toolkit - the underlying Python library