| --- |
| 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`, and `BANK_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 Face `datasets`, 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` + `language` for per‑language calibration. |
| - **NER / Token classification** – use `pii` spans 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 on `medium` risk. |
|
|
| --- |
|
|
| ## License |
|
|
| This dataset is released under the **MIT License**. |
