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Privacy-preserving releasing of complex data (e.g., image, text, audio) represents a long-standing challenge for the data mining research community.
Differential privacy
Dwork, C · 2006
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The differential privacy frontier (extended abstract)
Dwork, C · 2009
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Privacy integrated queries: An extensible platform for privacy-preserving data analysis
McSherry, F. D · 2009
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Learning in a large function space: Privacy-preserving mechanisms for svm learning
Rubinstein, B. I., Bartlett, P. L., Huang, L., and Taft, N · 2009
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Boosting and differential privacy
Dwork, C., Rothblum, G. N., and Vadhan, S. P · 2010
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Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
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What can we learn privately?
Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., and Smith, A. D · 2011
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An introduction to restricted boltzmann machines
Fischer, A., and Igel, C · 2012
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Differential privacy for protecting multi-dimensional contingency table data: Extensions and applications
Yang, X., Fienberg, S. E., and Rinaldo, A · 2012
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Functional mechanism: regression analysis under differential privacy
Zhang, J., Zhang, Z., Xiao, X., Yang, Y., and Winslett, M · 2012
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Auto-encoding variational bayes
Kingma, D. P., and Welling, M · 2013
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Stochastic gradient descent with differentially private updates
Song, S., Chaudhuri, K., and Sarwate, A. D · 2013
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Differential privacy preserving spectral graph analysis
Wang, Y., Wu, X., and Wu, L · 2013
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Differentially private histogram publication
Xu, J., Zhang, Z., Xiao, X., Yang, Y., Yu, G., and Winslett, M · 2013
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Bounds on the sample complexity for private learning and private data release
Beimel, A., Brenner, H., Kasiviswanathan, S. P., and Nissim, K · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Dwork, C., and Roth, A · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., and Szegedy, C · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Cited alongside, same era.
Deep learning with differential privacy
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Differentially private k-means clustering
Su, D., Cao, J., Li, N., Bertino, E., and Jin, H · 2016
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Semantic image inpainting with perceptual and contextual losses
Yeh, R., Chen, C., Lim, T., Hasegawa-Johnson, M., and Do, M. N · 2016
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Energy-based generative adversarial network
Zhao, J., Mathieu, M., and LeCun, Y · 2016
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Differential privacy for collaborative filtering recommender algorithm
Zhu, X., and Sun, Y · 2016
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Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
Cited alongside, same era.
Donahue, J., Krähenbühl, P., and Darrell, T · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A. P., Tejani, A., Totz, J., Wang, Z., and Shi, W · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Privacy-preserving generative deep neural networks support clinical data sharing
Beaulieu-Jones, B. K., Wu, Z. S., Williams, C., and Greene, C. S · 2017
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Differentially private mixture of generative neural networks
Gergely, A., Luca, M., Claude, C., and Emiliano, D. C · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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Improved semi-supervised learning with gans using manifold invariances
Kumar, A., Sattigeri, P., and Fletcher, P. T · 2017
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Adaptive laplace mechanism: Differential privacy preservation in deep learning
NhatHai, P., Xintao, W., Han, H., and Dejing, D · 2017
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Adversarial generation of natural language
Rajeswar, S., Subramanian, S., Dutil, F., Pal, C., and Courville, A · 2017
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Apple’s ‘differential privacy’ policy invoked for opt-in icloud data analysis in ios 10.3
Wuerthele, M · 2017
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