In this paper, a meta-heuristics model proposed to protect the confidentiality of data through anonymization. The aim is to minimize information loss as well as the maximization of privacy protection using Genetic algorithms and fuzzy sets. As a case study, Kohonen Maps put in practice through Self Organizing Map (SOM) applied to test the validity of the proposed model. SOM suffers from some privacy gaps and also demands a computationally, highly complex task. The experimental results show an improvement of protection of sensitive data without compromising cluster quality and optimality.
CITATION STYLE
Amiri, F., Quirchmayr, G., Kieseberg, P., Weippl, E., & Bertone, A. (2019). Towards Data Anonymization in Data Mining via Meta-heuristic Approaches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11737 LNCS, pp. 39–48). Springer. https://doi.org/10.1007/978-3-030-31500-9_3
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