metadata
license: mit
language:
- zh
- ar
- hi
- en
- es
- bn
- pt
- ru
- ja
- de
- ko
- fr
- jv
- te
- mr
- vi
- ta
- it
- tr
- ur
- pa
- uk
- gu
- th
- pl
tags:
- pii
- private
- pii-detection
- privacy
pretty_name: PRIVAset
size_categories:
- 1M<n<10M
PRIVAset: A Multilingual Synthetic PII Detection Dataset
PRIVAset is a large-scale, privacy-safe, synthetic dataset for training and evaluating Personally Identifiable Information (PII) filtering systems. It supports 25 languages, includes 16 PII types, and is designed for tasks such as classification, named-entity recognition (NER), and redaction.
All data is generated using Faker (locale-aware) and custom synthetic logic. No real PII is used, making it safe for public release, model fine-tuning, and benchmarking.
Dataset Highlights
- 16 PII types – including
PERSON_NAME,EMAIL,PHONE,SSN,CREDIT_CARD,IPV4/6,STREET_ADDRESS,DATE_OF_BIRTH,PASSPORT,DRIVERS_LICENSE,IBAN,TAX_ID,API_KEY,MEDICAL_RECORD, andBANK_ACCOUNT. - 25 languages – with native-script templates and locale-aware Faker support. Languages range from high-resource (English, Chinese, Arabic) to lower-resource (Javanese, Gujarati, Punjabi).
- 10 real-world contexts – including chat, email, support tickets, medical notes, legal documents, code snippets, social posts, resumes, ecommerce, and finance.
- Balanced positives and negatives – with hard negatives that look like PII but are not.
- Token‑level annotations – each record includes character-level spans (
start,end,type,value), document‑level label (has_pii), and risk score (high/medium/low). - Multi‑PII examples – ~30% of records contain 2–4 PII entities.
- Robustness augmentations – including email obfuscation (
[at]/(dot)), phone format variance, and case flipping. - Flexible exports – available in
JSONL,CSV, Hugging Facedatasets, and instruction‑tuning formats (prompt/completion).
Supported Languages
| Language | Code | Faker Locale | Native Script |
|---|---|---|---|
| Chinese | zh |
zh_CN |
中文 |
| Arabic | ar |
ar_SA |
العربية |
| Hindi | hi |
hi_IN |
हिन्दी |
| English | en |
en_US |
English |
| Spanish | es |
es_ES |
Español |
| Bengali | bn |
bn_BD |
বাংলা |
| Portuguese | pt |
pt_PT |
Português |
| Russian | ru |
ru_RU |
Русский |
| Japanese | ja |
ja_JP |
日本語 |
| German | de |
de_DE |
Deutsch |
| Korean | ko |
ko_KR |
한국어 |
| French | fr |
fr_FR |
Français |
| Javanese | jv |
id_ID |
Basa Jawa |
| Telugu | te |
en_IN |
తెలుగు |
| Marathi | mr |
mr_IN |
मराठी |
| Vietnamese | vi |
vi_VN |
Tiếng Việt |
| Tamil | ta |
ta_IN |
தமிழ் |
| Italian | it |
it_IT |
Italiano |
| Turkish | tr |
tr_TR |
Türkçe |
| Urdu | ur |
en_PK |
اردو |
| Punjabi | pa |
en_IN |
ਪੰਜਾਬੀ |
| Ukrainian | uk |
uk_UA |
Українська |
| Gujarati | gu |
gu_IN |
ગુજરાતી |
| Thai | th |
th_TH |
ภาษาไทย |
| Polish | pl |
pl_PL |
Polski |
Use Cases
- Multilingual PII classification – use
has_pii+languagefor per‑language calibration. - NER / Token classification – use
piispans to generate BIO tags; spans are character‑accurate for CJK and Arabic scripts. - Redaction systems – replace spans with
[REDACTED_{TYPE}]. - Risk‑aware filtering – block
high‑risk entities (SSN, credit card, IBAN, API key) and warn onmediumrisk.
License
This dataset is released under the MIT License.