Data Mask Studio vs SDSA

Data Mask Studio is a free Windows desktop application for masking sensitive data in CSV files and local HTML content. Users can choose columns to preserve, mask, or exclude; normalize, rename, or compose columns; and reuse profiles for batch work. CSV files can be processed individually or in batches, with selective restoration available later. Its deterministic tokens can keep repeated values consistent across records and files. The app can also restore known codes in local HTML files and dashboards while preserving their markup. Original values and mappings remain in an encrypted local vault, which is needed for restoration. The maker says tokens use HMAC-SHA256, vault data uses AES-256-GCM, and Windows DPAPI protects local keys. Processing stays local, without telemetry or automatic synchronization. Additional tools include password-protected portable backups, vault integrity audits, diagnostics, temporary-file cleanup, and controlled compaction. Large files use bounded-memory streaming. Releases offer a per-user Windows installer and portable ZIP. The app is licensed under GPL-3.0-only. It does not guarantee irreversible anonymization, and its executables are not digitally signed yet, so Windows may show a SmartScreen warning.

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.