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Plausible Deniability for Privacy-Preserving Data Synthesis

Summary: Plausible deniability: a privacy criterion for data synthesis, independent of adversary; testable. Efficient synthetic-data generation preserves statistics and ML utility; with proper randomness, it yields differential privacy on big datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11732
Venue
VLDB
Year
2017
Pagerank
7.8484858e-05
Overall Rank
3,016 | 79.31%
DOI
10.14778/3055540.3055542

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{bindschaedler_vldb17,
        title = {{Plausible Deniability for Privacy-Preserving Data Synthesis}},
        author = {Bindschaedler, Vincent and Shokri, Reza and Gunter, Carl A.},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {5},
        pages = {481},
        doi = {10.14778/3055540.3055542},
        url = {https://doi.org/10.14778/3055540.3055542},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
105 Quickly Generating Billion-Record Synthetic Databases 1994 SIGMOD 0.00033877899
338 Generalizing Data to Provide Anonymity when Disclosing Information 1998 PODS 0.00020731054
1,169 PrivBayes: Private Data Release via Bayesian Networks 2014 SIGMOD 0.00011838753
1,230 No Free Lunch in Data Privacy 2011 SIGMOD 0.00011572271
1,578 Simple and Realistic Data Generation 2006 VLDB 0.00010309264
2,856 Publishing Set-Valued Data via Differential Privacy 2011 VLDB 8.0321774e-05
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