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A common approach to synthetic data is to sample from a fitted model.
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Statistical disclosure limitation
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Diagnosing bootstrap success
Beran, R. (1997) · 1997
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Stochastic simulations conditioned on sufficient statistics
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A proof of the fisher information inequality via a data processing argument
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Asymptotic statistics
Van der Vaart, A. W. (2000) · 2000
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Satisfying disclosure restrictions with synthetic data sets
Reiter, J. P. (2002) · 2002
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Categorical data analysis
Agresti, A. (2003) · 2003
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Information preserving statistical obfuscation
Burridge, J. (2003) · 2003
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A theoretical basis for perturbation methods
Muralidhar, K. and R. Sarathy (2003) · 2003
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Multiple imputation for statistical disclosure limitation
Raghunathan, T. E., J. P. Reiter, and D. B. Rubin (2003) · 2003
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Elements of large-sample theory
Lehmann, E. L. (2004) · 2004
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Fast generation of accurate synthetic microdata
Mateo-Sanz, J. M., A. Martínez-Ballesté, and J. Domingo-Ferrer (2004) · 2004
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Monte Carlo conditioning on a sufficient statistic
Lindqvist, B. H. and G. Taraldsen (2005) · 2005
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Using CART to generate partially synthetic public use microdata
Reiter, J. P. (2005) · 2005
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Romm methodology for microdata release
Ting, D., S. Fienberg, and M. Trottini (2005) · 2005
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Sequential importance sampling for multiway tables
Chen, Y., I. H. Dinwoodie, and S. Sullivant (2006) · 2006
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Our data, ourselves: Privacy via distributed noise generation
Dwork, C., K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor (2006) · 2006
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Calibrating noise to sensitivity in private data analysis
Dwork, C., F. McSherry, K. Nissim, and A. Smith (2006) · 2006
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Conditional Monte Carlo based on sufficient statistics with applications
Lindqvist, B. H. and G. Taraldsen (2007) · 2007
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The multiple adaptations of multiple imputation
Reiter, J. P. and T. E. Raghunathan (2007) · 2007
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Accounting for intruder uncertainty due to sampling when estimating identification disclosure risks in partially synthetic data
Drechsler, J. and J. P. Reiter (2008) · 2008
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Privacy: Theory meets practice on the map
Machanavajjhala, A., D. Kifer, J. Abowd, J. Gehrke, and L. Vilhuber (2008) · 2008
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Some generalized functions for the size distribution of income
McDonald, J. B. (2008) · 2008
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Approximation theorems of mathematical statistics
Serfling, R. J. (2009) · 2009
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Random forests for generating partially synthetic, categorical data
Caiola, G. and J. P. Reiter (2010) · 2010
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Synthetic two-way contingency tables that preserve conditional frequencies
Slavković, A. B. and J. Lee (2010) · 2010
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Bun, M. and T. Steinke (2016) · 2016
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Concentrated differential privacy
Dwork, C. and G. N. Rothblum (2016) · 2016
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Model-based differentially private data synthesis
Liu, F. (2016) · 2016
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Rényi differential privacy
Mironov, I. (2017) · 2017
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Privbayes: Private data release via Bayesian networks
Zhang, J., G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao (2017) · 2017
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Differentially private uniformly most powerful tests for binomial data
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How can we analyze differentially-private synthetic datasets?
Charest, A.-S. (2011) · 2011
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Synthetic datasets for statistical disclosure control: theory and implementation
Drechsler, J. (2011) · 2011
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An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets
Drechsler, J. and J. P. Reiter (2011) · 2011
Cited alongside, same era.
What can we learn privately?
Kasiviswanathan, S. P., H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith (2011) · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Smith, A. (2011) · 2011
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A simple and practical algorithm for differentially private data release
Hardt, M., K. Ligett, and F. McSherry (2012) · 2012
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Awan, J. and A. Slavković (2018) · 2018
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Composable and versatile privacy via truncated cdp
Bun, M., C. Dwork, G. N. Rothblum, and T. Steinke (2018) · 2018
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PATE-GAN: Generating synthetic data with differential privacy guarantees
Jordon, J., J. Yoon, and M. van der Schaar (2018) · 2018
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A privacy preserving algorithm to release sparse high-dimensional histograms
Li, B., V. Karwa, A. Slavković, and R. C. Steorts (2018) · 2018
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Conditional fiducial models
Taraldsen, G. and B. H. Lindqvist (2018) · 2018
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Generating differentially private datasets using GANs
Triastcyn, A. and B. Faltings (2018) · 2018
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Differentially private generative adversarial network
Xie, L., K. Lin, S. Wang, F. Wang, and J. Zhou (2018) · 2018
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Simulation-based bias correction methods for complex models
Guerrier, S., E. Dupuis-Lozeron, Y. Ma, and M.-P. Victoria-Feser (2019) · 2019
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GANobfuscator: Mitigating information leakage under GAN via differential privacy
Xu, C., J. Ren, D. Zhang, Y. Zhang, Z. Qin, and K. Ren (2019) · 2019
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Differentially private inference for binomial data
Awan, J. and A. Slavković (2020) · 2020
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Private mean estimation of heavy-tailed distributions
Kamath, G., V. Singhal, and J. Ullman (2020) · 2020
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Dp-merf: Differentially private mean embeddings with random features for practical privacy-preserving data generation
Harder, F., K. Adamczewski, and M. Park (2021) · 2021
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Balancing inferential integrity and disclosure risk via model targeted masking and multiple imputation
Jiang, B., A. E. Raftery, R. J. Steele, and N. Wang (2021) · 2021
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Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling
Barber, R. F. and L. Janson (2022) · 2022
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Gaussian differential privacy
Dong, J., A. Roth, and W. J. Su (2022) · 2022
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Parametric bootstrap for differentially private confidence intervals
Ferrando, C., S. Wang, and D. Sheldon (2022) · 2022
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Data augmentation MCMC for Bayesian inference from privatized data
Ju, N., J. Awan, R. Gong, and V. Rao (2022) · 2022
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PCPs and the hardness of generating synthetic data
Ullman, J. and S. Vadhan (2020) · 2078
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