| Autor: |
Preen, Richard J., Albashir, Maha, Davy, Simon, Smith, Jim |
| Rok vydání: |
2022 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
This article presents the automatic checking of research outputs package acro, which assists researchers and data governance teams by automatically applying best-practice principles-based statistical disclosure control (SDC) techniques on-the-fly as researchers conduct their analyses. acro distinguishes between: research output that is safe to publish; output that requires further analysis; and output that cannot be published because it creates substantial risk of disclosing private data. This is achieved through the use of a lightweight Python wrapper that sits over well-known analysis tools that produce outputs such as tables, plots, and statistical models. This adds functionality to (i) identify potentially disclosive outputs against a range of commonly used disclosure tests; (ii) apply disclosure mitigation strategies where required; (iii) report reasons for applying SDC; and (iv) produce simple summary documents trusted research environment staff can use to streamline their workflow. The major analytical programming languages used by researchers are supported: Python, R, and Stata. The acro code and documentation are available under an MIT license at https://github.com/AI-SDC/ACRO |
| Databáze: |
arXiv |
| Externí odkaz: |
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