Differential privacy protection algorithm for network sensitive information based on singular value decomposition

Abstract

In order to reduce the risk of data privacy disclosure and improve the effect of information privacy protection, a differential privacy protection algorithm for network sensitive information based on singular value decomposition is proposed. TF-IDF method is used to extract network sensitive information text. By comparing the word frequency of network sensitive information, high word frequency word elements in network information content are collected to obtain the mining results of network sensitive information text. According to the decision tree theory, the equal difference privacy budget allocation mechanism is improved to achieve equal difference privacy budget allocation. By discarding some small singular values and corresponding spectral vectors, the data can be disturbed, and the availability of the original data can be retained, so that it can truly represent the original data set structure. According to the results of equal difference privacy budget allocation and singular value decomposition disturbance, the data of high-dimensional network graph is reduced by random projection, singular value decomposition is performed on the reduced data, and Gaussian noise is added to the singular value. Finally, the matrix to be published is generated through the inverse operation of singular value decomposition to achieve differential privacy protection of network sensitive information. The experimental results show that the privacy protection quality of this algorithm is high and the data availability is effectively improved.