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Data sanitization in the context of Internet of Things (IoT) privacy refers to the process of permanently and irreversibly hiding all sensitive information from vast amounts of streaming data. Taking into account the dynamic and real-time characteristics of streaming IoT data, we propose a parallel evolutionary Privacy-Preserving Data Mining (PPDM), called High-performance Evolutionary Data Sanitization for IoT (HEDS4IoT), and implement two mechanisms on a Graphics Processing Units (GPU)-aided parallelized platform to achieve real-time streaming protected data transmission. The first mechanism...

Due to the wide adoption and deployment of the Internet of Things (IoT), massive amounts of data are being generated and shared across various sectors. Privacy disclosure is a major threat in IoT-related applications if collected data is directly outsourced. In IoT environments with large datasets, the existing Privacy-Preserving Data Mining (PPDM) mechanisms are inefficient and not scalable. To deal with this shortcoming, we develop a novel evolutionary PPDM framework, namely GPU-Enabled PPDM for IoT (GEPI), using GPUs at the edge layer to make the PPDM both efficient and exhibiting usefulnes...