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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
11544
Venue
VLDB
Year
2017
Pagerank
7.2467347e-05
Overall Rank
3,304 | 77.02%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
145 Quickly Generating Billion-Record Synthetic Databases 1994 SIGMOD 0.0004138408
225 Generalizing Data to Provide Anonymity when Disclosing Information 1998 PODS 0.00032707646
1,446 PrivBayes: Private Data Release via Bayesian Networks 2014 SIGMOD 0.0001194108
1,465 No Free Lunch in Data Privacy 2011 SIGMOD 0.00011860847
1,483 Simple and Realistic Data Generation 2006 VLDB 0.00011720317
3,097 Publishing Set-Valued Data via Differential Privacy 2011 VLDB 7.5647028e-05
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