Research on Data Mining Technology of Internet Privacy Protection based on local differential privacy

Abstract

With the rapid development of social economy and science and technology, data sharing and analysis technology is one of the contents widely used in the Internet field. Collecting and distributing terminal data and mining analysis is the normal form of big data analysis. Under the condition of mutual distrust between the collector and the terminal data owner, privacy protection in data collection and analysis will directly affect the application results of big data analysis. Nowadays, localized differential privacy has been applied in data acquisition and data mining analysis. Although there are still many problems in relevant theories and technologies, with the continuous in-depth research of scientific researchers, relevant issues have been proposed according to practical cases, which not only effectively control the privacy budget loss, but also improve the accuracy of data application. Therefore, on the basis of understanding the concept of localized differential privacy and related models, and according to the current application status of Internet privacy protection data mining technology, this paper proposes a clustering method with LDP GMMC as the core. The final experimental results show that the improved data acquisition method can better meet the privacy protection requirements in the non-spherically distributed data scenario.