Prasser Fabian, Kohlmayer Florian, Lautenschläger Ronald, Kuhn Klaus A
Technische Universität München, München, Germany.
AMIA Annu Symp Proc. 2014 Nov 14;2014:984-93. eCollection 2014.
Collaboration and data sharing have become core elements of biomedical research. Especially when sensitive data from distributed sources are linked, privacy threats have to be considered. Statistical disclosure control allows the protection of sensitive data by introducing fuzziness. Reduction of data quality, however, needs to be balanced against gains in protection. Therefore, tools are needed which provide a good overview of the anonymization process to those responsible for data sharing. These tools require graphical interfaces and the use of intuitive and replicable methods. In addition, extensive testing, documentation and openness to reviews by the community are important. Existing publicly available software is limited in functionality, and often active support is lacking. We present ARX, an anonymization tool that i) implements a wide variety of privacy methods in a highly efficient manner, ii) provides an intuitive cross-platform graphical interface, iii) offers a programming interface for integration into other software systems, and iv) is well documented and actively supported.
合作与数据共享已成为生物医学研究的核心要素。特别是当来自分布式数据源的敏感数据被链接时，必须考虑隐私威胁。统计披露控制通过引入模糊性来保护敏感数据。然而，数据质量的降低需要与保护方面的收益相平衡。因此，需要有工具能为数据共享负责人提供匿名化过程的良好概述。这些工具需要图形界面，并使用直观且可复制的方法。此外，广泛的测试、文档记录以及对社区评审的开放性也很重要。现有的公开可用软件功能有限，且往往缺乏积极的支持。我们展示了ARX，这是一种匿名化工具，它具有以下特点：一、以高效的方式实现了多种隐私方法；二、提供直观的跨平台图形界面；三、提供用于集成到其他软件系统的编程接口；四、文档完善且有积极的支持。
