Multiple Strategies Differential Privacy on Sparse Tensor Factorization for Network Traffic Analysis in 5G

Abstract

Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension reduction to analyze high-order, high-dimension, and sparse tensor (HOHDST) data, which is transmitted on 5G Internet-of-things (IoT). Hence, HOHDST data relies on STF to obtain complete data and discover rules for real time and accurate analysis. From another view of computation and data security, the current STF solution seeks to improve the computational efficiency but neglects privacy security of the IoT data, e.g., data analysis for network traffic monitor system. To overcome these problems, this article proposes a multiple-strategies differential privacy framework on STF ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDPSTF</small> ) for HOHDST network traffic data analysis. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDPSTF</small> comprises three differential privacy (DP) mechanisms, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon -$</tex-math></inline-formula> DP, concentrated DP, and local DP. Furthermore, the theoretical proof of privacy bound is presented. Hence, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDPSTF</small> can provide general data protection for HOHDST network traffic data with high-security promise. We conduct experiments on two real network traffic datasets ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Abilene$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G\grave{E}ANT$</tex-math></inline-formula> ). The experimental results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDPSTF</small> has high universality on the various degrees of privacy protection demands and high recovery accuracy for the HOHDST network traffic data.