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Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements.
Understanding hierarchical methods for differentially private histograms
Qardaji, W., Yang, W., and Li, N · 1965
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A limited memory algorithm for bound constrained optimization
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C · 1995
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Beck, A. and Teboulle, M · 2003
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., and Talwar, K · 2007
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Mechanism design via differential privacy
McSherry, F. and Talwar, K · 2007
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Graphical models, exponential families, and variational inference
Wainwright, M. J. and Jordan, M. I · 2008
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Choosing a variable to clamp
Eaton, F. and Ghahramani, Z · 2009
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Probabilistic graphical models: principles and techniques
Koller, D. and Friedman, N · 2009
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Primal-dual subgradient methods for convex problems
Nesterov, Y · 2009
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A multiplicative weights mechanism for privacy-preserving data analysis
Hardt, M. and Rothblum, G. N · 2010
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On the geometry of differential privacy
Hardt, M. and Talwar, K · 2010
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Boosting the accuracy of differentially private histograms through consistency
Hay, M., Rastogi, V., Miklau, G., and Suciu, D · 2010
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Optimizing linear counting queries under differential privacy
Li, C., Hay, M., Rastogi, V., Miklau, G., and McGregor, A · 2010
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Probabilistic inference and differential privacy
Williams, O. and McSherry, F · 2010
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Dual averaging methods for regularized stochastic learning and online optimization
Xiao, L · 2010
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Differential privacy via wavelet transforms
Xiao, X., Wang, G., and Gehrke, J · 2010
Cited alongside, same era.
Differentially private data cubes: optimizing noise sources and consistency
Ding, B., Winslett, M., Han, J., and Li, Z · 2011
Cited alongside, same era.
Lsmr: An iterative algorithm for sparse least-squares problems
Fong, D. C.-L. and Saunders, M · 2011
Cited alongside, same era.
Privately releasing conjunctions and the statistical query barrier
Gupta, A., Hardt, M., Roth, A., and Ullman, J · 2011
Cited alongside, same era.
Differentially private histogram publishing through lossy compression
Acs, G., Castelluccia, C., and Chen, R · 2012
Cited alongside, same era.
Proximal algorithms
Parikh, N., Boyd, S., et al · 2014
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Calibrating Data to Sensitivity in Private Data Analysis
Proserpio, D., Goldberg, S., and McSherry, F · 2014
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Priview: Practical differentially private release of marginal contingency tables
Qardaji, W., Yang, W., and Li, N · 2014
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Towards accurate histogram publication under differential privacy
Zhang, X., Chen, R., Xu, J., Meng, X., and Xie, Y · 2014
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Differentially private high-dimensional data publication via sampling-based inference
Chen, R., Xiao, Q., Zhang, Y., and Xu, J · 2015
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Maximum likelihood postprocessing for differential privacy under consistency constraints
Lee, J., Wang, Y., and Kifer, D · 2015
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Differentially private spatial decompositions
Cormode, G., Procopiuc, M., Srivastava, D., Shen, E., and Yu, T · 2012
Cited alongside, same era.
A simple and practical algorithm for differentially private data release
Hardt, M., Ligett, K., and McSherry, F · 2012
Cited alongside, same era.
Faster algorithms for privately releasing marginals
Thaler, J., Ullman, J., and Vadhan, S · 2012
Cited alongside, same era.
Learning graphical model parameters with approximate marginal inference
Domke, J · 2013
Cited alongside, same era.
The geometry of differential privacy: the approximate and sparse cases
Nikolov, A., Talwar, K., and Zhang, L · 2013
Cited alongside, same era.
Accurate and efficient private release of datacubes and contingency tables
Yaroslavtsev, G., Cormode, G., Procopiuc, C. M., and Srivastava, D · 2013
Cited alongside, same era.
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The matrix mechanism: optimizing linear counting queries under differential privacy
Li, C., Miklau, G., Hay, M., McGregor, A., and Rastogi, V · 2015
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Autograd: Effortless gradients in numpy
Maclaurin, D., Duvenaud, D., and Adams, R. P · 2015
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Bethe projections for non-local inference
Vilnis, L., Belanger, D., Sheldon, D., and McCallum, A · 2015
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Differentially private learning of undirected graphical models using collective graphical models
Bernstein, G., McKenna, R., Sun, T., Sheldon, D., Hay, M., and Miklau, G · 2017
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Privbayes: Private data release via bayesian networks
Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D., and Xiao, X · 2017
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Eugenio, E. C. and Liu, F · 2018
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Optimizing error of high-dimensional statistical queries under differential privacy
McKenna, R., Miklau, G., Hay, M., and Machanavajjhala, A · 2018
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Ektelo: A framework for defining differentially-private computations
Zhang, D., McKenna, R., Kotsogiannis, I., Hay, M., Machanavajjhala, A., and Miklau, G · 2018
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