data privacy / Python
piiscope
Find, score, and remediate personal data in files and databases.
piiscope scans tabular data and free text for direct identifiers, then measures privacy risk across quasi-identifiers. It combines practical detection with k-anonymity, l-diversity, t-closeness, and column-level remediation workflows.
published benchmark and repository fixture
All 512 structured benchmark cases pass before a clean remediation rescan.
The public synthetic benchmark covers 32 multilingual positive and hard-negative families across 512 rows. The committed customers fixture separately exercises the full remediation round trip.
observed output
benchmark 512 / 512 exactprecision 1.000recall 1.000rescan 0 / low
what it exposes
Capabilities
- Scan CSV, JSON, JSONL, Parquet, text files, directories, and supported database sources
- Map findings to GDPR, CCPA, KVKK, and LGPD categories
- Measure k-anonymity, l-diversity, and t-closeness on quasi-identifiers
- Generate machine-readable reports and apply column-level remediation
workflow integration
Keep the evidence in the run
Run a privacy gate and emit SARIF without matched values.
- uses: barissozudogru/piiscope@v1.3.0good fit
Use it when
- Checking datasets before sharing, analysis, or model training
- Adding a repeatable personal-data check to local and CI workflows
- Investigating privacy risk beyond simple regex matches
operating limits
Know before using
- Detection is heuristic and should be reviewed against the context of the dataset
- A clean scan is not proof of legal or regulatory compliance
- Optional Parquet and NLP features require their matching extras