ARX Data Anonymization Tool vs SDSA

ARX Data Anonymization Tool is open-source software for anonymizing sensitive personal data and examining privacy risk and data utility. Its graphical application supports data import and cleansing, transformation-rule setup, tailoring anonymized datasets, and analysis of utility and residual re-identification risk. It imports CSV, MS Excel, and relational database data, including MS SQL, DB2, MySQL, and PostgreSQL. Listed methods include k-anonymity, l-diversity, t-closeness, δ-presence, risk-based criteria, and semantic models such as differential privacy. ARX exports displayed tables to CSV and compares input with transformed data using information-loss models, descriptive statistics, and application-specific analyses. Its risk analysis reports prosecutor, journalist, and marketer attack risks, estimates population uniqueness, and can identify HIPAA Safe Harbor identifiers and further quasi-identifiers. A Java library and API can add anonymization capabilities to Java programs. The tool is free under Apache License 2.0 and supports Linux, macOS, Windows, API, and self-hosted options. The maker provides PGP/GPG-signed checksums for download integrity and states the software is provided without warranties or conditions.

SDSA is a free, AGPL-3.0 self-hostable application for sanitizing tabular data before it leaves a trusted environment. It accepts CSV, delimited TXT, single-table SQL INSERT dumps, JSON records, and XML records through a browser interface or REST API. SDSA infers schemas and flags likely sensitive fields such as emails, phone numbers, card numbers, government IDs, dates of birth, names, and addresses. Transformations include masking, HMAC hashing, tokenization, redaction, dropping, numeric binning, date or string truncation, and bounded Laplace noise. Users can enforce k-anonymity for selected quasi-identifiers and measure or enforce l-diversity for sensitive cleartext attributes. The app exports sanitized CSV plus JSON and Markdown privacy reports with utility summaries. A headless command-line pipeline supports CI/CD and data pipelines. Sessions use in-memory storage for a default 30 minutes. Docker and Python installation are documented.