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

Summary: PrivAGM combines differential privacy, secure multiparty computation, and generative graph modeling to privately fit directed attributed graph models from decentralized local views. Key novelty: MPC-enabled DP training that preserves edge directionality and attribute–edge correlations, yielding synthetic graphs with substantially higher analytic utility than prior methods. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14077
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,759 | 25.16%
DOI
10.14778/3749646.3749722

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