![]() Finally, we discuss practical experiences with ARX and present remaining issues and challenges ahead.Ĭloud computing is a recent and fast growing area of development in healthcare. The results of an extensive experimental comparison show that our approach outperforms related solutions in terms of scalability and output data quality-while supporting a much broader range of techniques. We then review the spectrum of methods supported and discuss their compatibility within the novel framework. In this work, we describe how we have extended an open source data anonymization tool to support almost arbitrary combinations of a wide range of techniques in a scalable manner. In spite of these requirements, existing solutions typically only support a small set of methods. For instance, the effectiveness of different anonymization techniques depends on context, and thus tools need to support a large set of methods to ensure that the usefulness of data is not overly affected by risk‐reducing transformations. The development of anonymization tools involves significant challenges, however. Data anonymization is an important building block of data protection concepts, as it allows to reduce privacy risks by altering data. New data protection regulations, for example in the EU and China, are direct responses to these developments. As a consequence, adequate and careful privacy management has become a significant challenge. Examples include financial transactions, social network activities, location traces, and medical records. Rapid developments of new technologies, especially in the field of artificial intelligence, are accompanied by new ways of accessing, integrating, and analyzing sensitive personal data. The race for innovation has turned into a race for data.
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