ESRA 2027 sessions by theme

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Session Details

As research data expands to include complex, high-dimensional sources such as survey, health, administrative, geographical, smart data and many others, manual Statistical Disclosure Control (SDC) methods are becoming a significant bottleneck for data sharing and output checking. This sits within the wider challenge of sharing detailed research data as broadly as possible while protecting confidentiality and security, and keeping access swift and free of unnecessary barriers.
To balance timely data sharing with robust governance, safety and participant privacy, data custodians increasingly rely on semi-automated tools, e.g. automated global disclosure risk assessments, machine learning-driven risk scanning, automated k-anonymity and l-diversity checks. However, automated metrics often fail to capture contextual, real-world re-identification risks, and just as often inflate theoretical risk.
The aim of this session is to provide a platform for colleagues to discuss the intersection of semi-automated disclosure risk assessment and human methodological oversight. We invite presentations and papers addressing the following issues:
• Open-source or proprietary software for automated risk detection and SDC execution.
• Strategies to integrate domain expertise into semi-automated SDC workflows.
• Real-world case studies from data archives, national statistical institutes, or Trusted Research Environments (TREs) implementing hybrid risk-assessment pipelines.
By bringing together data managers, survey statisticians, and software developers, this session aims to establish best practices for human-machine collaboration in data governance, ensuring timely and safe data access.