Abstract
Nowadays, it has become a common practice to provide public access to various kinds of primary non-aggregated statistical data. Necessary precautions ought to be taken in order to guarantee that sensitive data features are masked, and data privacy cannot be violated. In the case of protecting information about a group of people, it is important to protect intrinsic data features and distributions. To do so, it is obligatory to introduce a certain level of distortion into the dataset. The problem of minimizing this distortion is a complex optimization task, which can be successfully solved by applying appropriate heuristic procedures, e.g., memetic algorithms. The task of determining whether a particular solution masks sensitive data features is an ill-defined one and often can be solved only by expert evaluation. In the paper, we propose to apply two-phase memetic algorithm to solve such tasks of providing group anonymity, for which it is not always possible to define appropriate constraints.
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Chertov, O., Tavrov, D. (2016). Two-Phase Memetic Modifying Transformation for Solving the Task of Providing Group Anonymity. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_17
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DOI: https://doi.org/10.1007/978-3-319-32229-2_17
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