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PrivAGM: Secure Construction of Differentially Private Directed Attributed Graph Models on Decentralized Social Graphs

Summary: PrivAGM combines differential privacy and secure multiparty computation to fit directed, attributed graph models when users hold only local views of a decentralized social graph. It preserves edge directionality and attribute–edge correlations, yielding higher-utility synthetic graphs than prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14265
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,992 | 24.59%
DOI
10.14778/3749646.3749722

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BibTeX Citation

@article{wang_vldb25,
        title = {{PrivAGM: Secure Construction of Differentially Private Directed Attributed Graph Models on Decentralized Social Graphs}},
        author = {Wang, Songlei and Zheng, Yifeng and Jia, Xiaohua and Hu, Haibo},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {11},
        pages = {4682--4694},
        doi = {10.14778/3749646.3749722},
        url = {https://doi.org/10.14778/3749646.3749722},
        year = {2025}
}

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