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Data Synthesis via Differentially Private Markov Random Fields

Summary: PrivMRF selects a flexible set of low-dimensional marginals M to build an MRF for DP data synthesis, instead of fixed marginals. This captures richer attribute correlations and outperforms prior DP methods on counting queries and classification across four benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12397
Venue
VLDB
Year
2021
Pagerank
7.9665978e-05
Overall Rank
2,881 | 79.96%
DOI
10.14778/3476249.3476272

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