Runners, repeaters, strangers and aliens: Operationalising efficient output disclosure control

Abstract

Statistical agencies and other government bodies increasingly use secure remote research facilities to provide access to sensitive data for research and analysis by internal staff and third parties. Such facilities depend on human intervention to ensure that the research outputs do not breach statistical disclosure control (SDC) rules. Output SDC can be principles-based, rules-based, or something in between. Principles-based is often seen as the gold standard statistically, as it improves both confidentiality protection and utility of outputs. However, some agencies are concerned that the operational requirements are too onerous for practical implementation, despite these statistical advantages. This paper argues that the choice of output checking procedure should be seen through an operational lens, rather than a statistical one. We take a popular conceptualisation of customer demand from the operations management literature and apply it to the problem of output checking. We demonstrate that principles-based output SDC addresses user and agency requirements more effectively than other approaches, and in a way which encourages user buy-in to the process. We also demonstrate how the principles-based approach aligns better with the statistical and staffing needs of the agency.

Next-Generation Capabilities in Trusted Research Environments: Interview Study

“…Typically, the 2 different models for output checking are principle-based and rule-based models [ 36 , 37 ]. The principle-based model better ensures confidentiality protection and utility of outputs from a statistical perspective, at the expense of being hard to standardize, automate, and verify.…”

“…TREs must implement suitable review and management processes to further ensure that researchers are using the TREs appropriately. Best practice approaches to delivering effective training for TRE researchers, which not only support the use of the TRE but also facilitate building shared attitudes and responsibilities in protecting data, are widely available [ 36 , 37 , 51 ].…”

Restricted data management: the current practice and the future

“…Disclosure review rules and procedures vary by types of data and access modes. Alves and Ritchie (2020) articulate two approaches to managing output-vetting: “rules-based” and “principle-based.” The rules-based approach establishes a certain set of strict rules regarding disclosive information and scrutinizes research outputs created from restricted data based on the rules. On the other hand, the principle-based approach allows flexible negotiation between researchers and output vetting staff.…”

“…Most organizations support a pool of experts to perform disclosure risk reviews, which is often time- and resource-consuming. Instead, organizations may consider an automated disclosure review system since output checking for disclosure risks is not necessarily a statistical matter but an operational matter ( Alves and Ritchie, 2020 ). In fact, some organizations have already implemented a machine-driven output checking for disclosure risks with regard to relatively simple matters such minimum cell thresholds, although other organizations still rely rely on human powers for the output checking.…”

Using the Five Safes to structure economic evaluations of data governance

“…New data protection regulations and the value of sharing data with others have raised the importance of data governance (the processes and procedures to ensure that data use and sharing is ethical, fair and appropriate). Data governance is often seen as a cost, but there are arguments that it should be seen as an investment (Ritchie, 2021; Green and Ritchie, 2023), and there is clear evidence of the ability of good data governance to reduce costs (e.g., Alves and Ritchie, 2020). The COVID-19 pandemic proved a strong test for data governance systems in both the public and private sectors.…”