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, the Parallel Indexing Engine (PIE), generates retrieval index lists from the dataset using GPU blocks. These lists are used in place of the dataset during the PPDM process. The second mechanism, called Parallel Fitness Function Engine (PF2E), parallelizes the index lists on the GPU threads to speed up the computation of the quality of solutions generated by the evolutionary algorithm, in which d
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 usefulness for IoT applications. On the one hand, the evolutionary algorithm used in the PPDM can guarantee the high utility by selecting the best candidate transactions for modification, providing a shareable dataset with minimum modifications and maximum privacy. On the other hand, the evolutionary algorithm is parallelized using a developed GPU based mechanism to accelerate database scans. In our mechan