Data Mask Studio vs Open Diffix

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.

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.