A privacy-aware data sharing framework for Internet of Things through edge computing platform

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 mechanism, the dataset is distributed among the GPU threads to compute the fitness value in a parallel manner. Tests with extensive benchmarks reveal that our mechanism can accelerate the fitness function step 53.7x on average. The findings also show that the developed PPDM algorithm achieves an average speedup of 43.8x and 47.3x when compared to the state-of-the-art algorithms of ABC4ARH and PACO2DT, respectively.