A General Cipher for Individual Data Anonymization

Abstract

Over the years, the literature on individual data anonymization has burgeoned in many directions. While such diversity should be praised, it does not come without some difficulties. Currently, the task of selecting the optimal analytical environment is complicated by the multitude of available choices and the fact that the performance of any method is generally dependent of the data properties. In light of these issues, the contribution of this paper is twofold. First, based on recent insights from the literature and inspired by cryptography, it proposes a new anonymization method that shows that the task of anonymization can ultimately rely only on ranks permutations. As a result, the method offers a new way to practice data anonymization by performing it ex-ante and independently of the distributional features of the data instead of being engaged, as it is currently the case in the literature, in several ex-post evaluations and iterations to reach the protection and information properties sought after. Second, the method establishes a conceptual connection across the field, as it can mimic all the currently existing tools. To make the method operational, this paper proposes also the introduction of permutation menus in data anonymization, where recently developed universal measures of disclosure risk and information loss are used ex-ante for the calibration of permutation keys. To justify the relevance of their uses, a theoretical characterization of these measures is also proposed.