Publishing data about individuals, in a privacy-preserving way, has led to a large body of research. Meanwhile, algorithms for anonymizing datasets, with relational or transaction attributes, that preserve data truthfulness, have attracted significant interest from organizations. However, selecting the most appropriate algorithm is still far from trivial, and tools that assist data publishers in this task are needed. In response, we develop SECRETA, a system for analyzing the effectiveness and efficiency of anonymization algorithms. Our system allows data publishers to evaluate a specific algorithm, compare multiple algorithms, and combine algorithms for anonymizing datasets with both relational and transaction attributes. The analysis of the algorithm(s) 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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