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We investigate the problem of learning discrete, undirected graphical models in a differentially private way.
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On information and sufficiency
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Log-linear models for frequency data: Sufficient statistics and likelihood equations
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Practical privacy: the SuLQ framework
Blum, Avrim, Dwork, Cynthia, McSherry, Frank, and Nissim, Kobbi · 2005
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Calibrating noise to sensitivity in private data analysis
Dwork, Cynthia, McSherry, Frank, Nissim, Kobbi, and Smith, Adam · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Barak, Boaz, Chaudhuri, Kamalika, Dwork, Cynthia, Kale, Satyen, McSherry, Frank, and Talwar, Kunal · 2007
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Efficient projections onto the l 1-ball for learning in high dimensions
Duchi, John, Shalev-Shwartz, Shai, Singer, Yoram, and Chandra, Tushar · 2008
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Efficient, differentially private point estimators
Smith, Adam · 2008
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Graphical models, exponential families, and variational inference
Wainwright, Martin J. and Jordan, Michael I · 2008
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Privacy-preserving logistic regression
Chaudhuri, Kamalika and Monteleoni, Claire · 2009
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Probabilistic graphical models: principles and techniques
Koller, Daphne and Friedman, Nir · 2009
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Learning in a large function space: Privacy-preserving mechanisms for SVM learning
Rubinstein, Benjamin I.P., Bartlett, Peter L., Huang, Ling, and Taft, Nina · 2009
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Probabilistic inference and differential privacy
Williams, Oliver and McSherry, Frank · 2010
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Differentially private empirical risk minimization
Chaudhuri, Kamalika, Monteleoni, Claire, and Sarwate, Anand D · 2011
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What can we learn privately?
Kasiviswanathan, Shiva Prasad, Lee, Homin K., Nissim, Kobbi, Raskhodnikova, Sofya, and Smith, Adam · 2011
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Collective graphical models
Sheldon, Daniel R. and Dietterich, Thomas G · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Smith, Adam · 2011
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Maximum likelihood estimation in log-linear models
Fienberg, Stephen E. and Rinaldo, Alessandro · 2012
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A simple and practical algorithm for differentially private data release
Hardt, Moritz, Ligett, Katrina, and McSherry, Frank · 2012
Cited alongside, same era.
Differentially private exponential random graphs
Karwa, Vishesh, Slavković, Aleksandra B., and Krivitsky, Pavel · 2014
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Gaussian approximation of collective graphical models
Liu, Li-Ping, Sheldon, Daniel R., and Dietterich, Thomas G · 2014
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Privbayes: Private data release via Bayesian networks
Zhang, Jun, Cormode, Graham, Procopiuc, Cecilia M., Srivastava, Divesh, and Xiao, Xiaokui · 2014
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DPT: differentially private trajectory synthesis using hierarchical reference systems
He, Xi, Cormode, Graham, Machanavajjhala, Ashwin, Procopiuc, Cecilia M., and Srivastava, Divesh · 2015
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Message passing for collective graphical models
Sun, Tao, Sheldon, Daniel R., and Kumar, Akshat · 2015
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Bethe projections for non-local inference
Vilnis, Luke, Belanger, David, Sheldon, Daniel, and McCallum, Andrew · 2015
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Private convex empirical risk minimization and high-dimensional regression
Kifer, Daniel, Smith, Adam, and Thakurta, Abhradeep · 2012
Cited alongside, same era.
Differential privacy for protecting multi-dimensional contingency table data: Extensions and applications
Yang, Xiaolin, Fienberg, Stephen E., and Rinaldo, Alessandro · 2012
Cited alongside, same era.
Differentially private learning with kernels
Jain, Prateek and Thakurta, Abhradeep · 2013
Cited alongside, same era.
Approximate inference in collective graphical models
Sheldon, Daniel R., Sun, Tao, Kumar, Akshat, and Dietterich, Thomas G · 2013
Cited alongside, same era.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Bassily, Raef, Smith, Adam, and Thakurta, Abhradeep · 2014
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Robust and private Bayesian inference
Dimitrakakis, Christos, Nelson, Blaine, Mitrokotsa, Aikaterini, and Rubinstein, Benjamin I.P · 2014
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Privacy for free: Posterior sampling and stochastic gradient Monte Carlo
Wang, Yu-Xiang, Fienberg, Stephen, and Smola, Alex · 2015
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Deep learning with differential privacy
Abadi, Martín, Chu, Andy, Goodfellow, Ian, McMahan, H. Brendan, Mironov, Ilya, Talwar, Kunal, and Zhang, Li · 2016
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On the theory and practice of privacy-preserving Bayesian data analysis
Foulds, James, Geumlek, Joseph, Welling, Max, and Chaudhuri, Kamalika · 2016
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Inference using noisy degrees: Differentially private b e t a beta -model and synthetic graphs
Karwa, Vishesh, Slavković, Aleksandra, et al · 2016
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Approximate inference using DC programming for collective graphical models
Nguyen, Duc Thien, Kumar, Akshat, Lau, Hoong Chuin, and Sheldon, Daniel · 2016
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Differentially private stochastic gradient descent for in-RDBMS analytics
Wu, Xi, Kumar, Arun, Chaudhuri, Kamalika, Jha, Somesh, and Naughton, Jeffrey F · 2016
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On the differential privacy of Bayesian inference
Zhang, Zuhe, Rubinstein, Benjamin I.P., and Dimitrakakis, Christos · 2016
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