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We summarize the experience of participating in two differential privacy competitions organized by the National Institute of Standards and Technology (NIST).
Differentially private mixed-type data generation for unsupervised learning
U. Tantipongpipat, C. Waites, D. Boob, A. A. Siva, and R. Cummings · 1912
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Network flows
R. K. Ahuja, T. L. Magnanti, and J. B. Orlin · 1988
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New constructions for covering designs
D. M. Gordon, O. Patashnik, and G. Kuperberg · 1995
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Revealing information while preserving privacy
I. Dinur and K. Nissim · 2003
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry, and K. Talwar · 2007
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The price of privacy and the limits of LP decoding
C. Dwork, F. McSherry, and K. Talwar · 2007
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Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
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A learning theory approach to non-interactive database privacy
A. Blum, K. Ligett, and A. Roth · 2008
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New efficient attacks on statistical disclosure control mechanisms
C. Dwork and S. Yekhanin · 2008
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On the complexity of differentially private data release: efficient algorithms and hardness results
C. Dwork, M. Naor, O. Reingold, G. Rothblum, and S. Vadhan · 2009
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Discovering frequent patterns in sensitive data
R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta · 2010
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Boosting and differential privacy
C. Dwork, G. Rothblum, and S. Vadhan · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
M. Hardt and G. N. Rothblum · 2010
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Boosting the accuracy of differentially private histograms through consistency
M. Hay, V. Rastogi, G. Miklau, and D. Suciu · 2010
Cited alongside, same era.
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
S. P. Kasiviswanathan, M. Rudelson, A. Smith, and J. Ullman · 2010
Cited alongside, same era.
Optimizing linear counting queries under differential privacy
C. Li, M. Hay, V. Rastogi, G. Miklau, and A. McGregor · 2010
Cited alongside, same era.
Differentially private data cubes: optimizing noise sources and consistency
B. Ding, M. Winslett, J. Han, and Z. Li · 2011
Cited alongside, same era.
Privately releasing conjunctions and the statistical query barrier
A. Gupta, M. Hardt, A. Roth, and J. Ullman · 2011
Cited alongside, same era.
Pcps and the hardness of generating private synthetic data
Dual query: Practical private query release for high dimensional data
M. Gaboardi, E. J. G. Arias, J. Hsu, A. Roth, and Z. S. Wu · 2014
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Priview: practical differentially private release of marginal contingency tables
W. Qardaji, W. Yang, and N. Li · 2014
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Differentially private high-dimensional data publication via sampling-based inference
R. Chen, Q. Xiao, Y. Zhang, and J. Xu · 2015
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
M. Bun and T. Steinke · 2016
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Plausible deniability for privacy-preserving data synthesis
V. Bindschaedler, R. Shokri, and C. A. Gunter · 2017
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Privbayes: Private data release via bayesian networks
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao · 2017
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J. Ullman and S. Vadhan · 2011
Cited alongside, same era.
The multiplicative weights update method: a meta-algorithm and applications
S. Arora, E. Hazan, and S. Kale · 2012
Cited alongside, same era.
Submodular functions are noise stable
M. Cheraghchi, A. Klivans, P. Kothari, and H. K. Lee · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Privbasis: Frequent itemset mining with differential privacy
N. Li, W. Qardaji, D. Su, and J. Cao · 2012
Cited alongside, same era.
Faster algorithms for privately releasing marginals
J. Thaler, J. Ullman, and S. P. Vadhan · 2012
Cited alongside, same era.
On differentially private frequent itemset mining
C. Zeng, J. F. Naughton, and J.-Y. Cai · 2012
Cited alongside, same era.
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Privacy preserving synthetic data release using deep learning
N. C. Abay, Y. Zhou, M. Kantarcioglu, B. Thuraisingham, and L. Sweeney · 2018
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Calm: Consistent adaptive local marginal for marginal release under local differential privacy
Z. Zhang, T. Wang, N. Li, S. He, and J. Chen · 2018
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Privacy-preserving generative deep neural networks support clinical data sharing
B. K. Beaulieu-Jones, Z. S. Wu, C. Williams, R. Lee, S. P. Bhavnani, J. B. Byrd, and C. S. Greene · 2019
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Differentially private generative adversarial networks for time series, continuous, and discrete open data
L. Frigerio, A. S. de Oliveira, L. Gomez, and P. Duverger · 2019
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Graphical-model based estimation and inference for differential privacy
R. Mckenna, D. Sheldon, and G. Miklau · 2019
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Covering designs repository maintained by Dan Gordon, 2021
D. Gordon · 2021
Closest in time.
Privsyn: Differentially private data synthesis
Z. Zhang, T. Wang, N. Li, J. Honorio, M. Backes, S. He, J. Chen, and Y. Zhang · 2021
Closest in time.