Large-scale data privacy-preserving method based on data perturbation
jing xiong · International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022) · 2022
Internet of things (IoT) uses perception technology and intelligent equipment to perceive and identify the physical world, interconnect through network transmission, and perform calculation, processing and knowledge mining. Millions of IoT devices are connected to IoT, generating a large amount of data, and the application of data mining technology is gradually deepening. Meanwhile, the privacy protection aspect in IoT environment has become one of the hot issues. In this paper, by describing the characteristics and privacy threats of IoT large-scale data, analyzing the shortcomings of existing data mining privacy protection methods, we propose a data perturbation privacy protection method based on Gaussian mixture model. The method generates and discloses a set of new data with independent and same distribution as the original data to perturb the original data, which not only effectively protects the privacy of the original data, but also maintains the statistical characteristics and has similar accuracy to the mining model generated on the original data.