Abstract
Due to the growing use of IoT and 5G technologies, data are collected at an unprecedented pace. These data are used to improve decision-making processes. However, they could endanger individuals privacy, which is protected by international regulations. In this article, we propose a privacy-preserving microaggregation technique, inspired by the Travelling Salesman Problem, to protect individuals privacy through k-anonymity. We recall the basics on microaggregation and the TSP and, we describe the algorithm behind our approach. Also, we report experiments with real benchmark data sets showing that our approach outperforms current methods for low cardinality values.
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