BoB14TeamSentinel/sentinel-kr-sensitive-entities-synthetic-v3
Sentinel KR Sensitive Entities (Synthetic) v3 Overview Sentinel KR Sensitive Entities (Synthetic) v3 is a Korean synthetic (AI-generated) dataset for whitelist-only sensitive-entity detection in DLP / LLM guardrail scenarios. All sensitive values in this dataset (e.g., phone numbers, emails, IDs, tokens, keys) are artificially generated by AI and do not come from real individuals, real incidents, or collected private datasets. Any resemblance to real persons or… See the full description on the dataset page: https://huggingface.co/datasets/BoB14TeamSentinel/sentinel-kr-sensitive-entities-synthetic-v3.
Sentinel KR Sensitive Entities (Synthetic) v3
Overview
Sentinel KR Sensitive Entities (Synthetic) v3 is a Korean synthetic (AI-generated) dataset for whitelist-only sensitive-entity detection in DLP / LLM guardrail scenarios.
All sensitive values in this dataset (e.g., phone numbers, emails, IDs, tokens, keys) are artificially generated by AI and do not come from real individuals, real incidents, or collected private datasets.
Any resemblance to real persons or real identifiers is purely coincidental.
Intended Use
- Train / evaluate models that detect sensitive information before sending prompts or text payloads to external LLM services.
- Build DLP-style guardrails (block / warn / mask / redact) for enterprise environments.
- Benchmark Korean sensitive-entity extraction for PII + secret/token patterns.
Not Intended Use
- Re-identification, deanonymization, or tracking of real individuals.
- Assuming real-world distributions (this is synthetic data and may differ from production traffic).
Data Format
- Primary format: JSON / JSONL (chat-style SFT samples)
- Columns
id(int): sample identifiermessages(list): chat messages in SFT style, typically 3 turns:system: instruction to act as a strict whitelist-only detectoruser: input textassistant: target JSON output (no extra text)
Output Contract (Assistant Message)
The assistant content is a JSON string and follows this schema:
{
"text": "<original input text verbatim>",
"has_sensitive": <boolean>,
"entities": [
{
"value": "<exact substring as it appears in the text>",
"begin": <integer>, // 0-based char offset (inclusive)
"end": <integer>, // 0-based char offset (exclusive)
"label": "<UPPER_SNAKE_CASE category>"
}
]
}Whitelist Label Set (Allowed Labels)
Models trained on this dataset are expected to only output the following labels:
Basic identity
NAME— Person namePHONE— Phone numberEMAIL— Email addressADDRESS— Address (road name / district / detailed address)POSTAL_CODE— Postal/ZIP code
Government / official identifiers
PERSONAL_CUSTOMS_ID— Personal Customs Clearance Code (KR)RESIDENT_ID— Resident Registration Number (KR)PASSPORT— Passport numberDRIVER_LICENSE— Driver’s license numberFOREIGNER_ID— Foreigner registration numberHEALTH_INSURANCE_ID— Health insurance IDBUSINESS_ID— Business registration numberMILITARY_ID— Military service number
Authentication / secrets
JWT— JSON Web TokenAPI_KEY— API key (vendor-agnostic)GITHUB_PAT— GitHub Personal Access TokenPRIVATE_KEY— Private key material (SSH/TLS/PGP)
Financial
CARD_NUMBER— Card numberCARD_EXPIRY— Card expiry (MM/YY etc.)BANK_ACCOUNT— Bank account numberCARD_CVV— CVC/CVVPAYMENT_PIN— Payment/ATM PINMOBILE_PAYMENT_PIN— Mobile payment PIN
Crypto
MNEMONIC— Recovery seed phrase / mnemonicCRYPTO_PRIVATE_KEY— Crypto private keyHD_WALLET— HD wallet extended keyPAYMENT_URI_QR— Payment URI / QR payload (BTC/ETH/XRP/SOL/TRON etc.)
Network / device
IPV4— IPv4 addressIPV6— IPv6 addressMAC_ADDRESS— MAC addressIMEI— IMEI
Splits
train: ~52.9k rows (v3 release)
Recommendation: If you addvalidation/testsplits later, consider releasing them under a new version (e.g., v4) for reproducibility.
How to Load (Hugging Face Datasets)
from datasets import load_dataset
ds = load_dataset("BoB14TeamSentinel/sentinel-kr-sensitive-entities-synthetic-v3")
print(ds["train"][0])Quality Notes / Limitations
- Synthetic generation may not perfectly match real-world traffic (domain shift).
- Some patterns may be over/under-represented depending on the generation prompts.
- Ambiguous numeric strings may cause false positives in some settings.
Safety & Ethics
- This dataset was created to reduce privacy risk by using synthetic data only.
- Do not use it for real-person identification or any privacy-invasive purpose.
License
This dataset is released under CC BY 4.0. You must provide appropriate attribution when using or redistributing it.
Citation / Attribution
If you use this dataset, please attribute:
- BoB14TeamSentinel, Sentinel KR Sensitive Entities (Synthetic) v3 (Hugging Face dataset)
Project
- Project: Sentinel Solution
- Organization: Team.될것같은데
