Abstract
We suggest a user-oriented approach to combinatorial data anonymization. A data matrix is called k-anonymous if every row appears at least k times—the goal of the NP-hard k -Anonymity problem then is to make a given matrix k-anonymous by suppressing (blanking out) as few entries as possible. We describe an enhanced k-anonymization problem called Pattern-Guided k -Anonymity where the users can express the differing importance of various data features. We show that Pattern-Guided k -Anonymity remains NP-hard. We provide a fixed-parameter tractability result based on a data-driven parameterization and, based on this, develop an exact ILP-based solution method as well as a simple but very effective greedy heuristic. Experiments on several real-world datasets show that our heuristic easily matches up to the established “Mondrian” algorithm for k -Anonymity in terms of quality of the anonymization and outperforms it in terms of running time.
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Bredereck, R., Nichterlein, A., Niedermeier, R. (2013). Pattern-Guided k-Anonymity. In: Fellows, M., Tan, X., Zhu, B. (eds) Frontiers in Algorithmics and Algorithmic Aspects in Information and Management. Lecture Notes in Computer Science, vol 7924. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38756-2_35
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