Libelle DataMasking vs Open Diffix

Libelle DataMasking has no write-up on Specifiction yet; its figures are above.

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.