Abstract
Publishing medical datasets about individuals, in a privacy-preserving way, has led to a significant body of research. Meanwhile, algorithms for anonymizing such datasets, with relational or set-valued (a.k.a. transaction) attributes, in a way that preserves data truthfulness, are crucial to medical research. Selecting, however, the most appropriate algorithm is still far from trivial, and tools that assist data publishers in this task are needed. To highlight this need, we initially provide a brief description of the popular anonymization algorithms and the most commonly used metrics to quantify data utility. Next, we present a system that we have designed, which is capable of applying a set of anonymization algorithms, enabling data holders, including medical researchers and healthcare organizations, to test the effectiveness and efficiency of different methods. Our system, called SECRETA, allows evaluating a specific anonymization algorithm, comparing multiple anonymization algorithms, and combining algorithms for anonymizing datasets with both relational and transaction attributes. The analysis of the algorithms is performed in an interactive and progressive way, and results, including attribute statistics and various data utility indicators, are summarized and presented graphically.
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Notes
- 1.
We note that in the case of RT-datasets containing more than one transaction attributes, these attributes can be integrated into a single transaction attribute by changing their domain and values’ specification. Assuming two such attributes X and Y, with X having values x 1, x 2 and Y values y 1, y 2, we can replace these attributes with a single transaction attribute, say Z, with domain {X. x 1, X. x 2, Y. y 1, Y. y 2}.
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Poulis, G., Gkoulalas-Divanis, A., Loukides, G., Skiadopoulos, S., Tryfonopoulos, C. (2015). SECRETA: A Tool for Anonymizing Relational, Transaction and RT-Datasets. In: Gkoulalas-Divanis, A., Loukides, G. (eds) Medical Data Privacy Handbook. Springer, Cham. https://doi.org/10.1007/978-3-319-23633-9_5
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DOI: https://doi.org/10.1007/978-3-319-23633-9_5
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