Research on Privacy Protection Methods for Data Mining in Large-scale and Massive Data
The Internet of Things environment generates a large amount of data,and the issue of data privacy protection has become one of the hot research fields.By describing he characteristics and privacy threats of big data in the Internet of Things,the shortcomings of existing privacy-preserving methods for data mining are analyzed,a privacy-preserving data perturbation method based on mixed Gaussian distribution is proposed.This method aims to achieve the purpose of perturbing the original data by generating and publicly releasing a set of new data that is independent and identically distributed as the original data.It effectively protects the privacy of the original data while maintaining the statistical characteristics of the original data,and has similar accuracy as the mining model generated on the original data.
Internet of Thingsdata miningdata perturbationhybrid Gauss-model
万方数据
维普
