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How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an untrusted third party.
Experiments with a new boosting algorithm
Y. Freund, R. E. Schapire, et al · 1996
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Greedy function approximation: a gradient boosting machine
J. H. Friedman · 2001
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k-anonymity: A model for protecting privacy
L. Sweeney · 2002
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Differential privacy
C. Dwork · 2006
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l-diversity: Privacy beyond k-anonymity
A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam · 2006
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Privtree: A differentially private algorithm for hierarchical decompositions
J. Zhang, X. Xiao, and X. Xie · 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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Approximating the kullback leibler divergence between gaussian mixture models
J. R. Hershey and P. A. Olsen · 2007
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Never walk alone: Uncertainty for anonymity in moving objects databases
O. Abul, F. Bonchi, and M. Nanni · 2008
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
F. D. McSherry · 2009
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Differentially private combinatorial optimization
A. Gupta, K. Ligett, F. McSherry, A. Roth, and K. Talwar · 2010
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Differentially private histogram publication
J. Xu, Z. Zhang, X. Xiao, Y. Yang, G. Yu, and M. Winslett · 2013
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Analyze gauss: optimal bounds for privacy-preserving principal component analysis
C. Dwork, K. Talwar, A. Thakurta, and L. Zhang · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Privbayes: private data release via bayesian networks
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao · 2014
Cited alongside, same era.
Differentially private high-dimensional data publication via sampling-based inference
R. Chen, Q. Xiao, Y. Zhang, and J. Xu · 2015
Cited alongside, same era.
Calibrating probability with undersampling for unbalanced classification
A. Dal Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi · 2015
Cited alongside, same era.
https://ergodicity.net/2017/04/07/retraction-for-symmetric-matrix-perturbation-for-differentially-private-principal-component-analysis-icassp-2016/
2016
Cited alongside, same era.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
The us census bureau adopts differential privacy
J. M. Abowd · 2018
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Differentially private mixture of generative neural networks
G. Acs, L. Melis, C. Castelluccia, and E. De Cristofaro · 2018
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Pate-gan: generating synthetic data with differential privacy guarantees
J. Jordon, J. Yoon, and M. van der Schaar · 2018
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Differentially private hierarchical count-of-counts histograms
Y.-H. Kuo, C.-C. Chiu, D. Kifer, M. Hay, and A. Machanavajjhala · 2018
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Differential privacy synthetic data challenge
NIST · 2018
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A differentially private index for range query processing in clouds
C. Sahin, T. Allard, R. Akbarinia, A. El Abbadi, and E. Pacitti · 2018
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M. Bun and T. Steinke · 2016
Cited alongside, same era.
Xgboost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
Cited alongside, same era.
Wishart mechanism for differentially private principal components analysis
W. Jiang, C. Xie, and Z. Zhang · 2016
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling · 2016
Cited alongside, same era.
The complexity of computing the optimal composition of differential privacy
J. Murtagh and S. Vadhan · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar · 2016
Cited alongside, same era.
Plausible deniability for privacy-preserving data synthesis
V. Bindschaedler, R. Shokri, and C. A. Gunter · 2017
Cited alongside, same era.
Subsampled r \ \backslash ’enyi differential privacy and analytical moments accountant
Y.-X. Wang, B. Balle, and S. Kasiviswanathan · 2018
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Differentially private generative adversarial network
L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou · 2018
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Disclosure avoidance and the 2020 census
U. S. C. Bureau · 2019
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Capacity bounded differential privacy
K. Chaudhuri, J. Imola, and A. Machanavajjhala · 2019
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Constrained private mechanisms for count data
G. Cormode, T. Kulkarni, and D. Srivastava · 2019
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Privatesql: a differentially private sql query engine
I. Kotsogiannis, Y. Tao, X. He, M. Fanaeepour, A. Machanavajjhala, M. Hay, and G. Miklau · 2019
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rmckenna - differential privacy synthetic data challenge algorithm
R. McKenna · 2019
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General confidentiality and utility metrics for privacy-preserving data publishing based on the permutation model
J. Domingo-Ferrer, K. Muralidhar, and M. Bras-Amorós · 2020
Closest in time.
Don’t generate me: Training differentially private generative models with sinkhorn divergence
T. Cao, A. Bie, A. Vahdat, S. Fidler, and K. Kreis · 2021
Closest in time.