Real-time Dynamic Data Desensitization Method based on Data Stream

With the rapid development of the data mining industry, the value hidden in the massive data has been discovered, but at the same time it has also raised concerns about privacy leakage, leakage of sensitive data and other issues. These problems have also become numerous studies. Among the methods for solving these problems, data desensitization technology has been widely adopted for its outstanding performance. However, with the increasing scale of data and the increasing dimension of data, the traditional desensitization method for static data can no longer meet the requirements of various industries in today's environment to protect sensitive data. In the face of ever-changing data sets of scale and dimension, static desensitization technology relies on artificially designated desensitization rules to grasp the massive data, and it is difficult to control the loss of data connotation. In response to these problems, this paper proposes a real-time dynamic desensitization method based on data flow, and combines the data anonymization mechanism to optimize the data desensitization strategy. Experiments show that this method can efficiently and stably perform real-time desensitization of stream data, and can save more information to support data mining in the next steps.