Greenmask vs Open Diffix
Greenmask
github.comOpen Diffix
open-diffix.orgSide by side
| Figure | Greenmask | Open Diffix |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Data Masking Software | 7.9 (#8) | 7.2 (#17) |
Plans
In their own words
Greenmask
Greenmask is a free, open-source utility for logical database dumps, anonymization, synthetic data generation, and restoration. PostgreSQL 11 and later is fully supported; MySQL support is in progress and beta. Its subsetting tools can produce smaller development databases while preserving referential integrity, including cyclic and polymorphic references. Transformations may be deterministic or random, use dynamic parameters and conditional rules, and inherit across partitions and foreign-key references. Users can make custom transformers that exchange data through standard input and output, or use predefined templates. Greenmask supports local directories, S3-compatible storage, Azure Blob Storage, and SSH/SFTP. It uses PostgreSQL's pg_dump and pg_restore utilities for schema dumping and restoration, and its dumps can be restored with pg_restore. The utility is designed for CI/CD automation of anonymization and restoration. Its stateless, in-flight approach can anonymize data before it is persisted without requiring persistent storage of unmasked datasets in its normal workflow. Installation scripts support Linux and macOS; Windows is not supported yet.
Open Diffix
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.