The race for innovation has turned into a race for data. 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. Examples include financial transactions, social network activities, location traces, and medical records. As a consequence, adequate and careful privacy management has become a significant challenge. New data protection regulations, for example in the EU and China, are direct responses to these developments. Data anonymization is an important building block of data protection concepts, as it allows to reduce privacy risks by altering data. The development of anonymization tools involves significant challenges, however. 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. In spite of these requirements, existing solutions typically only support a small set of methods. 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. We then review the spectrum of methods supported and discuss their compatibility within the novel framework. 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. Finally, we discuss practical experiences with ARX and present remaining issues and challenges ahead.
The sharing of sensitive personal health data is an important aspect of biomedical research. Methods of data de-identification are often used in this process to trade the granularity of data off against privacy risks. However, traditional approaches, such as HIPAA safe harbor or -anonymization, often fail to provide data with sufficient quality. Alternatively, data can be de-identified only to a degree which still allows us to use it as required, e.g., to carry out specific analyses. Controlled environments, which restrict the ways recipients can interact with the data, can then be used to cope with residual risks. The contributions of this article are twofold. First, we present a method for implementing controlled data sharing environments and analyze its privacy properties. Second, we present a de-identification method which is specifically suited for sanitizing health data which is to be shared in such environments. Traditional de-identification methods control the uniqueness of records in a dataset. The basic idea of our approach is to reduce the probability that a record in a dataset has characteristics which are unique within the underlying population. As the characteristics of the population are typically not known, we have implemented a pragmatic solution in which properties of the population are modeled with statistical methods. We have further developed an accompanying process for evaluating and validating the degree of protection provided. The results of an extensive experimental evaluation show that our approach enables the safe sharing of high-quality data and that it is highly scalable.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.