Welcome to the AI-SDC family of tools#
Our tools are designed to help researchers assess the privacy disclosure risks of their outputs, including tables, plots, statistical models, and trained machine learning models
Statistical Disclosure Control for Python
Tools for the Semi-Automatic Checking of Research Outputs. Drop-in replacements for common analysis commands with built-in privacy protection.
Machine Learning Privacy Tools
Collection of tools and resources for managing the statistical disclosure control of trained machine learning models.
R Package Integration
R-language interface for the Python ACRO library, providing familiar R syntax for statistical disclosure control.
Graphical User Interface
A graphical user interface for fast, secure and effective output checking, which can work in any TRE (Trusted Research Environment).
ACRO: Statistical Disclosure Control#
ACRO is a free and open source tool that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. SACRO is a framework that applies best-practice principles-based statistical disclosure control (SDC) techniques on-the-fly as researchers conduct their analysis. SACRO is designed to assist human checkers rather than seeking to replace them as with current automated rules-based approaches.
Note
New in v0.4.8: Enhanced support for complex statistical models and improved R integration.
What is ACRO?#
ACRO implements a principles-based statistical disclosure control (SDC) methodology that:
Automatically identifies potentially disclosive outputs
Applies optional disclosure mitigation strategies
Reports reasons for applying SDC
Produces summary documents for output checkers
Core Features#
Semi-Automated Disclosure Checking#
Drop-in replacements for common Python analysis commands (pandas, statsmodels, etc.) with configurable disclosure checks
Automated sensitivity tests: frequency thresholds, dominance (p%, NK rules, etc.), residual degrees-of-freedom checks
Optional mitigations: suppression, rounding, and more to come
Session management: track, rename, comment, remove, add exceptions, and finalise reports
Configurable risk parameters via YAML files
Generates auditable reports in JSON or Excel
Design Principles#
Free and open source under MIT (ACRO) / GPLv3 (SACRO Viewer)
Easy to install via PyPI, CRAN, or GitHub; cross-platform (Linux, macOS, Windows)
Familiar APIs - same function signatures as native commands: acro.crosstab mirrors pandas.crosstab, etc.
Comprehensive coverage - tables, regressions, histograms, survival plots, etc.
Transparent & auditable - clear reports, stored queries, designed for human-checkers
Configurable & extensible - organisation-defined disclosure rules, multi-language support
Scalable - lightweight, session-based, local execution
Getting Started#
Get ACRO installed and configured in your environment
Explore tutorials and examples for common use cases
Complete API documentation and function reference
Key Methods#
Making tables - e.g.
crosstab()Regression analysis - e.g.
ols()Making plots - e.g.
hist()Managing a research session - e.g.
finalise()