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Differentially Private Data Generation with Missing Data

Summary: Formalizes DP synthetic-data generation with missing values and proposes three adaptive strategies that markedly improve utility across diverse missingness regimes. Models missingness as a sampling process to derive tighter bounds relating DP guarantees on incomplete inputs to privacy of the true complete data. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13436
Venue
VLDB
Year
2024
Pagerank
4.7180617e-05
Overall Rank
7,485 | 47.93%
DOI
10.14778/3659437.3659455

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Rank Citing Paper Year Venue Pagerank
10,162 Enhancing Local Differential Privacy Accuracy by Exploiting Inherent Uncertainty 2026 SIGMOD 4.1945683e-05
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