Kamino: Constraint-Aware Differentially Private Data Synthesis
Summary: Kamino synthesizes databases with differential privacy while explicitly preserving schema/integrity constraints and tuple–attribute dependencies. Unlike application-specific synthesizers, it maintains structural validity and correlations, improving downstream classification and marginal-query utility. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chang Ge (University of Waterloo)
- 2. Shubhankar Mohapatra (University of Waterloo)
- 3. Xi He (University of Waterloo)
- 4. Ihab F. Ilyas (University of Waterloo)
BibTeX Citation
@article{ge_vldb21,
title = {{Kamino: Constraint-Aware Differentially Private Data Synthesis}},
author = {Ge, Chang and Mohapatra, Shubhankar and He, Xi and Ilyas, Ihab F.},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {10},
pages = {1886--1899},
doi = {10.14778/3467861.3467876},
url = {https://doi.org/10.14778/3467861.3467876},
year = {2021}
}
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