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Safe Outputs & Disclosure Control

This domain covers the processes, techniques, and tools used to ensure that research outputs from Secure Data Environments do not inadvertently disclose sensitive information.

Statistical Disclosure Control

Statistical Disclosure Control employs mathematical and statistical techniques to protect sensitive information in research outputs. This subdomain encompasses assessing disclosure risks through comprehensive evaluation methodologies, applying appropriate control techniques that balance data protection with research utility, and implementing automated systems that enforce consistent application of disclosure controls across varied research outputs while maintaining statistical validity.

Disclosure Risk Assessment

Evaluates research outputs for potential privacy violations and re-identification risks. Involves identifying different types of disclosure risks (identity and attribute), conducting comprehensive assessments, analyzing complex outputs for subtle risks, recommending appropriate controls, developing assessment methodologies, and implementing advanced quantification approaches to enhance risk management.

  • Understands basic disclosure risk concepts
  • Familiar with common types of disclosure risks (identity, attribute)
  • Can identify obvious disclosure risks in simple outputs
  • Conducts comprehensive disclosure risk assessments
  • Analyses complex outputs for subtle disclosure risks
  • Recommends appropriate controls based on risk assessment
  • Develops disclosure risk assessment methodologies and frameworks
  • Implements advanced risk quantification approaches
  • Leads initiatives to enhance disclosure risk management

SDC Techniques & Methods

Applies statistical methods to protect confidentiality while preserving data utility. Involves implementing techniques like suppression and rounding, evaluating their effectiveness and impact, designing appropriate strategies for different outputs, developing advanced methodologies for complex scenarios, and establishing frameworks and best practices for the organization.

  • Understands common SDC techniques (suppression, rounding, etc.)
  • Familiar with when to apply different SDC methods
  • Can apply basic SDC techniques following established guidelines
  • Implements a wide range of SDC techniques for different data types
  • Evaluates effectiveness and impact of SDC methods
  • Designs SDC strategies appropriate to specific outputs
  • Develops advanced SDC methodologies for complex scenarios
  • Establishes SDC frameworks and best practices
  • Leads research into innovative SDC approaches

Automated Disclosure Control

Implements systems that automatically apply disclosure controls to research outputs. Involves using automated tools, implementing solutions for common output types, configuring rules and thresholds, validating effectiveness, architecting enterprise strategies, and developing advanced algorithms to enhance automated disclosure control capabilities.

  • Understands automated SDC tool concepts
  • Familiar with available SDC automation tools
  • Can use automated tools following established procedures
  • Implements automated SDC solutions for common output types
  • Configures SDC rules and thresholds in automation systems
  • Validates accuracy and effectiveness of automated controls
  • Architects enterprise automated SDC strategies
  • Develops advanced SDC algorithms and automation approaches
  • Leads initiatives to enhance automated SDC capabilities

Output Checking

Output Checking provides systematic review and verification of research outputs before they leave secure environments. This subdomain focuses on implementing structured review processes for different output formats, creating comprehensive documentation and justification records that support release decisions, and developing decision support systems that ensure consistent application of disclosure control rules while maintaining appropriate governance and traceability.

Output Review Processes

Reviews research outputs to ensure they meet disclosure control requirements before release. Involves understanding review requirements for different output formats, conducting reviews using established checklists, designing workflows, implementing tracking and governance processes, establishing frameworks and policies, and developing innovative approaches to improve efficiency.

  • Understands output review requirements and processes
  • Familiar with common output formats and their risks
  • Can conduct basic output reviews following established checklists
  • Designs output review workflows and documentation
  • Conducts thorough reviews of complex research outputs
  • Implements output tracking and governance processes
  • Establishes enterprise output review frameworks and policies
  • Develops innovative approaches to output review efficiency
  • Leads initiatives to mature output checking capabilities

Output Documentation & Justification

Creates records explaining the purpose and contents of research outputs to support safe release. Involves preparing documentation, understanding justification criteria, designing templates and standards, reviewing for completeness and validity, implementing management systems, establishing frameworks, and developing cross-organizational standards to enhance traceability.

  • Understands output documentation requirements
  • Familiar with justification criteria for different output types
  • Can prepare basic documentation for research outputs
  • Designs documentation templates and standards
  • Reviews output justifications for completeness and validity
  • Implements systems for managing output documentation
  • Establishes enterprise output documentation frameworks
  • Develops cross-organizational standards for output justification
  • Leads initiatives to enhance output traceability

Decision Support Systems

Develops tools and frameworks to guide consistent disclosure control decisions. Involves understanding decision criteria, using support tools, implementing systems for output checking, developing rules for different output types, evaluating effectiveness, architecting advanced frameworks, implementing AI/ML approaches, and leading initiatives to improve decision consistency and quality.

  • Understands the role of decision support in output checking
  • Familiar with decision criteria and guidelines
  • Can use decision support tools following established procedures
  • Implements decision support systems for output checking
  • Develops decision rules and logic for different output types
  • Evaluates effectiveness of decision support approaches
  • Architects advanced decision support frameworks for complex scenarios
  • Implements AI/ML approaches to enhance decision support
  • Leads initiatives to improve decision consistency and quality

Safe Data Publication

Safe Data Publication enables secure sharing of research data beyond secure environments. This subdomain encompasses implementing robust de-identification techniques that remove identifying information while preserving analytical value, generating synthetic datasets that maintain statistical properties without exposing real individuals' data, and adhering to established publication standards that ensure appropriate documentation, formatting, and accessibility while maintaining privacy protection.

De-identification Techniques

Removes or transforms identifying information in datasets to enable safer sharing. Involves understanding basic concepts, identifying direct and indirect identifiers, applying de-identification methods, implementing complex strategies, evaluating effectiveness, designing approaches for different data types, and developing advanced methodologies including cutting-edge techniques like differential privacy.

  • Understands basic de-identification concepts and techniques
  • Familiar with direct and indirect identifiers
  • Can apply simple de-identification methods following guidelines
  • Implements complex de-identification strategies
  • Evaluates and validates effectiveness of de-identification
  • Designs de-identification approaches for different data types
  • Develops advanced de-identification methodologies and frameworks
  • Implements cutting-edge techniques like differential privacy
  • Leads de-identification research and innovation

Synthetic Data Generation

Creates artificial datasets that preserve statistical properties without containing real individuals' data. Involves understanding synthetic data concepts, using generation tools, implementing pipelines, evaluating utility and privacy, designing approaches for specific research needs, developing advanced methodologies, and leading innovation in synthetic data generation techniques.

  • Understands synthetic data concepts and use cases
  • Familiar with basic synthetic data generation approaches
  • Can use synthetic data tools following established procedures
  • Implements synthetic data generation pipelines
  • Evaluates utility and privacy of synthetic datasets
  • Designs synthetic data approaches for specific research needs
  • Develops advanced synthetic data generation methodologies
  • Implements cutting-edge synthetic data technologies
  • Leads research into synthetic data innovation

Data Publication Standards

Ensures released data follows appropriate standards for documentation, format, and accessibility. Involves understanding publication requirements, preparing metadata and documentation, implementing workflows and processes, ensuring compliance with standards and policies, establishing frameworks and governance, and developing organizational standards aligned with best practices.

  • Understands data publication requirements and standards
  • Familiar with metadata and documentation needs
  • Can prepare data for publication following established templates
  • Implements data publication workflows and processes
  • Ensures compliance with relevant standards and policies
  • Designs documentation approaches for different data types
  • Establishes data publication frameworks and governance
  • Develops organizational standards aligned with best practices
  • Leads initiatives to enhance data sharing capabilities

Code Publication & Reusability

Makes research code available in a secure, reusable, and transparent manner following FAIR principles. Involves understanding code publication principles, using repositories and sharing platforms, preparing code for publication, implementing workflows, ensuring proper documentation, establishing frameworks and governance, and developing best practices while maintaining security.

  • Understands the principles of open code publication (FAIR principles)
  • Familiar with code repositories and sharing platforms
  • Can prepare code for publication following established templates
  • Implements code publication workflows and processes
  • Ensures code is well-documented and follows best practices
  • Addresses security concerns specific to SDEs when publishing code
  • Establishes code publication frameworks and governance
  • Develops best practices for code metadata and documentation
  • Leads initiatives to enhance code sharing while maintaining security