Abstract
Trying to solve the problem of weight information disclosure in weighted social network, a privacy preserving data publishing method named DWT-DP is proposed in the paper. After partitioning the social network into multiple communities by using Louvain algorithm, the DWT-DP method designs an adaptive allocation strategy for privacy budget based on modularity, to extend the life cycle of privacy budget and reduce the amount of injected noise. For each community, diffusion wavelets transform (DWT) is performed and Laplace noise is added to the corresponding DW tree. The DWT-DP method also presents a community re-connection algorithm to connect perturbed communities with certain probability for synthesizing a complete social network. Experimental results on two real datasets show that the proposed method achieves good data utility in condition of preserving sensitive weight information.
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Acknowledgments
This work is supported by the Fundamental Research Funds for the Central Universities (No. GK201906009), CERNET Innovation Project (No. NGII20190704), Science and Technology Program of Xi’an City (No. 2019216914GXRC005CG006-GXYD5.2), Key Research and Development Program of Shaanxi Province (No. 2021GY-090).
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Lei, H., Li, S. & Wang, H. A weighted social network publishing method based on diffusion wavelets transform and differential privacy. Multimed Tools Appl 81, 20311–20328 (2022). https://doi.org/10.1007/s11042-022-12726-1
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DOI: https://doi.org/10.1007/s11042-022-12726-1