Smart Anonymity: a mechanism for recommending data anonymization algorithms based on data profiles for IoT environments

Abstract

The Internet of Things (IoT) has grown rapidly in recent years. However, coupled with a large number of devices and the huge volume of data generated by these devices, an impasse arises: the privacy of the data of IoT device users. The goal of this research is to propose a solution that recommends which data anonymization algorithm is the most suitable for the dataset according to its characteristics. Among the contributions of this research, we can highlight the creation of metrics for classifying IoT data. Using the metrics to create two Ontologies that use Description Logic for classification of IoT data. The use of Description Logic brings as positive points the verification of inconsistency and discovery of new facts to validate data. The evaluation results demonstrate that the use of the proposed approach brings benefits for user privacy.