Core Concepts#

Understanding the fundamental concepts behind ACRO’s statistical disclosure control methodology.

Principles-Based SDC#

ACRO implements a principles-based approach to statistical disclosure control, focusing on:

Risk Assessment#

  • Automatic detection of disclosure risks

  • Context-aware evaluation of outputs

  • Proportionate response to identified risks

Human-in-the-Loop#

  • Researcher guidance rather than blocking

  • Transparent reasoning for all decisions

  • Flexible override capabilities for experts

Audit and Accountability#

  • Complete audit trails for all outputs

  • Reproducible workflows with version control

  • Clear documentation of all decisions

Disclosure Control Methods#

Cell Suppression#

Current implementation:

  • Primary suppression - Hide risky cells

Note

Roadmap Feature: Secondary and complementary suppression are planned for future releases.

Planned suppression methods:

  • Secondary suppression - Protect against inference

  • Complementary suppression - Additional protection

Statistical Perturbation#

Note

Roadmap Feature: Statistical perturbation methods are planned for future releases.

Planned perturbation methods:

  • Cell-level perturbation - Modify individual values

  • Table-level perturbation - Systematic adjustments

  • Controlled rounding - Round to safe multiples

Output Restriction#

Limiting what can be released:

  • Threshold enforcement - Minimum cell requirements

  • Aggregation requirements - Force higher-level summaries

  • Model coefficient restrictions - Limit regression detail

ACRO Implementation#

Safety Checks#

ACRO performs multiple safety checks:

  1. Threshold checks - Minimum observation counts

  2. Dominance checks - Concentration of values

  3. Model disclosure - Regression coefficient safety

Configuration System#

Flexible configuration through:

  • YAML configuration files - Environment-specific settings

  • Policy templates - Organizational standards

Note

Roadmap Feature: Method-specific runtime parameter overrides are planned for future releases.

Integration Points#

Data Analysis Libraries#

ACRO integrates with:

  • pandas - DataFrame operations and aggregations

  • statsmodels - Statistical modeling and regression

  • matplotlib/seaborn - Visualization with safety checks

Research Environments#

Designed for:

  • Trusted Research Environments (TREs) - Secure analysis platforms

  • Data enclaves - Controlled access environments

  • Multi-user systems - Collaborative research settings

Quality Assurance#

Validation Framework#

ACRO includes comprehensive validation:

  • Unit testing - Individual function verification

  • Integration testing - End-to-end workflow validation

  • Regression testing - Consistency across versions

Performance Monitoring#

Note

Roadmap Feature: Performance monitoring capabilities are planned for future releases.

Planned performance tracking features:

  • Execution timing - Analysis performance metrics

  • Memory usage - Resource consumption monitoring

  • Scalability testing - Large dataset handling

Best Practices#

Configuration Management#

  • Use version-controlled configuration files

  • Document all threshold customizations

  • Test configurations with sample data

Workflow Design#

  • Plan analysis workflows in advance

  • Use meaningful output names and descriptions

  • Implement regular checkpoint saves

Quality Control#

  • Review all disclosure warnings before finalizing

  • Validate results against expected patterns

  • Maintain detailed analysis documentation