DataVeil vs SDSA

DataVeil masks sensitive SQL database data with fictitious values for testing, development, training, analytics and support. It supports Microsoft SQL Server 2014 or later, Azure SQL DB and MI, MySQL 5.7 or later, Oracle 12c or later, and PostgreSQL 14 or later. Choose random masking or deterministic masking, which can keep values consistent across databases and separate runs. Masks can retain value formats and partial content to help preserve statistical distributions. Discovery algorithms check suspected identifiers and account numbers, and users can add regular-expression patterns. DataVeil maintains referential integrity for defined foreign keys and supports user-defined dependencies. Before writing masked values, users can compare originals and results side by side. Projects run from the GUI, command line or batch files. The Community license includes generated PDF reports and automatically multi-threaded masking. It runs as a Java client with an embedded JDK 21 and uses JDBC. The free Community License never expires and covers databases up to 10GB. The Team license is $5,690 a year for an example configuration; its price depends on connectivity options and source-data size.

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.