DATPROF vs Open Diffix

DATPROF is a test data management platform for creating, protecting, automating and delivering data for development, testing and QA. Its modules cover privacy, subsetting, runtime, analysis and virtualization. Privacy masks test data and creates synthetic data while retaining representative characteristics, with more than 50 built-in generators supporting different languages. Subset extracts selected production data for test environments while preserving relationships. Runtime supports self-service provisioning, scheduling, monitoring and REST API automation. DATPROF can be deployed on premise or in the cloud, and says data stays in its database domains during masking. Native database support includes Oracle, Microsoft SQL Server, DB2 iSeries, DB2 LUW, PostgreSQL, EDB, MySQL and MariaDB. Supported flat file formats include CSV, Fixed Length, Recursive Fixed Length and XML. DATPROF says its masking can support GDPR, PCI and HIPAA requirements. Its security program aligns with ISO 27001 principles and includes SAST, dependency scanning and DAST in development. Support is available through a service desk portal, email and a documentation knowledge base. Pricing is available on request, and a free trial is listed.

Open Diffix is a free, open-source project for anonymizing structured data, available through APIs and self-hosted deployments on Linux, macOS, and Windows. Its SynDiffix Python package generates statistically accurate synthetic data with strong anonymity. It is aimed especially at descriptive analysis, including histograms, heatmaps, column correlations, counts, averages, and standard deviations. You can generate a separate synthetic table for each analytic task and distribute a blob from which others create tables after installing SynDiffix. The project says this approach can preserve anonymity across many column combinations, but blobs currently do not scale well beyond about 10 columns. SynDiffix installs with pip and requires Python 3.10 or later. It can also support machine learning, though the maker says ML use is somewhat less easy. For data containing multiple rows per protected entity, provide the entity identifier separately or anonymity may be compromised. Open Diffix also includes pg_diffix, a PostgreSQL extension for aggregate anonymized answers to a very limited SQL subset; development is paused.