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

Jin Wang, Hui Han, Hao Li, Shiming He, Pradip Kumar Sharma, Lydia Chen

Research output: Contribution to journalArticleScientificpeer-review

77 Citations (Scopus)
54 Downloads (Pure)


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 (MDPSTF) for HOHDST network traffic data analysis. MDPSTF comprises three differential privacy (DP) mechanisms, i.e., varepsilon - DP, concentrated DP, and local DP. Furthermore, the theoretical proof of privacy bound is presented. Hence, MDPSTF can provide general data protection for HOHDST network traffic data with high-security promise. We conduct experiments on two real network traffic datasets (Abilene and Ggrave{E}ANT). The experimental results show that MDPSTF has high universality on the various degrees of privacy protection demands and high recovery accuracy for the HOHDST network traffic data.

Original languageEnglish
Article number9439054
Pages (from-to) 1939 - 1948
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number3
Publication statusPublished - 2021


  • Differential privacy framework
  • multiple-strategies privacy protection
  • network traffic analysis
  • sparse tensor factorization


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