Pattern-Guided k-Anonymity

Abstract

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 PatternGuided 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.