Differentially private discrete multi-attributed network releasing

Yinghao Lin; Yuye Wang; Shihao Zhao; Baojun Qiao

doi:10.1117/12.2674332

28 April 2023 Differentially private discrete multi-attributed network releasing

Yinghao Lin, Yuye Wang, Shihao Zhao, Baojun Qiao

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126262P (2023) https://doi.org/10.1117/12.2674332
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

In the area of social network, different attributes have different effects on the structure of network. Most of the existing privacy protection methods for attributed networks ignore the situation which different attributes have different effects on the network structure. They protect the privacy of the attributes indiscriminately. In respect of the issues above, a differentially private discrete multi-attributed network releasing method is proposed. Firstly, a probability model of discrete multi-attributed network is structured and the correlation parameter between multiple attributes and network structure is defined. The factor with different effects of different attributes on network structure is added into the model. Then, the algorithm uses the correlation parameter to establish the partition model of metadata and divides the metadata into different groups. As the group has different network model and attribute between each other, the groups are independence. The differential privacy of discrete multi-attributed network is realized through sanitizing parameters of the model and allocating metadata using exponential mechanism. Finally, experiment on real datasets verifies that the algorithm can satisfy the characteristics of the discrete multi-attributed network. It can also improve the efficiency and data availability.

Citation Download Citation

Yinghao Lin, Yuye Wang, Shihao Zhao, and Baojun Qiao "Differentially private discrete multi-attributed network releasing", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126262P (28 April 2023); https://doi.org/10.1117/12.2674332

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KEYWORDS

Data modeling

Social networks

Expectation maximization algorithms

Matrices

Data privacy

Connectors

Internet

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