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Data Synthesis via Differentially Private Markov Random Fields

Summary: PrivMRF synthesizes high-dimensional data under differential privacy by flexibly selecting low-dimensional marginals to construct a Markov random field capturing attribute correlations. It improves counting-query and classification accuracy over prior methods on four benchmarks. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12585
Venue
VLDB
Year
2021
Pagerank
8.5258582e-05
Overall Rank
2,476 | 83.02%
DOI
10.14778/3476249.3476272

Incoming Non-self Citations Over Time

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BibTeX Citation

@article{cai_vldb21,
        title = {{Data Synthesis via Differentially Private Markov Random Fields}},
        author = {Cai, Kuntai and Lei, Xiaoyu and Wei, Jianxin and Xiao, Xiaokui},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {11},
        pages = {2190--2202},
        doi = {10.14778/3476249.3476272},
        url = {https://doi.org/10.14778/3476249.3476272},
        year = {2021}
}

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