sdsa 1.2.0

Self-hostable data sanitization app with PII detection, pseudonymization, k-anonymity, and bounded DP noise

SDSA

Secure Data Sanitization App (SDSA) is a self-hostable tool for sanitizing tabular data before it leaves a trusted environment.

It ingests CSV, delimited TXT, and single-table SQL INSERT dumps; detects likely sensitive fields; applies explicit per-column privacy policies; enforces k-anonymity; measures or enforces l-diversity for sensitive cleartext attributes; and exports a sanitized CSV with JSON and Markdown privacy reports.

Install

pip install sdsa
sdsa-server start

Open http://127.0.0.1:8000/.

What It Does

• Serves a browser UI and REST API from one FastAPI application.
• Detects likely PII such as email, phone, card number, government ID, date of birth, name, and address fields.
• Supports retain, mask, hash, tokenize, redact, drop, numeric_bin, date_truncate, string_truncate, and dp_laplace actions.
• Applies bounded Laplace noise to numeric columns when differential privacy is configured with explicit epsilon, lower, and upper values.
• Tracks cumulative per-column DP epsilon for the uploaded session to prevent repeated noisy releases from being averaged.
• Enforces k-anonymity over selected quasi-identifiers and can enforce l-diversity on sensitive cleartext attributes.
• Provides preflight suppression estimates before processing.
• Stores uploaded data in memory with a default 30-minute session TTL.

CLI

sdsa-server start
sdsa-server start --host 0.0.0.0 --port 8000
sdsa-server start --random-port
sdsa-server start --reload

The package includes the static frontend, so no separate web build is required.

Privacy Model

SDSA produces pseudonymized microdata with optional per-column local-DP style noise. It does not claim dataset-level (epsilon, delta) differential privacy. Linkage attacks using auxiliary data may still succeed.

k-anonymity bounds prosecutor re-identification risk to at most 1/k for the declared quasi-identifier set, subject to the limits described in each generated privacy report.

l-diversity is measured by default for cleartext non-QI attributes and can be enforced with l >= 2. Homogeneous sensitive groups appear as warnings in the report when l-diversity is measured but not enforced.

Deployment

For production, run sdsa-server start behind TLS termination and keep one SDSA process per deployment unless you replace the in-memory session store with shared infrastructure. The GitHub repository includes Docker, Compose, nginx, and CI/CD examples.

Links

• Source: https://github.com/defai-digital/sdsa
• Documentation: https://github.com/defai-digital/sdsa/blob/main/README.md
• Deployment guide: https://github.com/defai-digital/sdsa/blob/main/docs/deployment.md
• Privacy model: https://github.com/defai-digital/sdsa/blob/main/docs/privacy-model.md