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Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations.
On an extremum problem of information theory
Csiszár, I. (1974) · 1974
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Information Theory: Coding Theorems for Discrete Memoryless Systems
Csiszar, I. and Korner, J. (1982) · 1982
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Methods of combining multiple classifiers based on different representations for pen-based handwriting recognition
Alimoglu, F. and Alpaydin, E. (1996) · 1996
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Coding facial expressions with gabor wavelets
Lyons, M., Akamatsu, S., Kamachi, M., and Gyoba, J. (1998) · 1998
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k-anonymity: A model for protecting privacy
Sweeney, L. (2002) · 2002
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Elements of Information Theory
Cover, T. M. and Thomas, J. A. (2006) · 2006
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Differential privacy
Dwork, C. (2006) · 2006
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l-diversity: Privacy beyond k-anonymity
Machanavajjhala, A., Gehrke, J., Kifer, D., and Venkitasubramaniam, M. (2006) · 2006
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t-closeness: Privacy beyond k-anonymity and l-diversity
Li, N., Li, T., and Venkatasubramanian, S. (2007) · 2007
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Gradient methods for minimizing composite objective function
Nesterov, Y. (2007) · 2007
Cited alongside, same era.
Differential Privacy: A Survey of Results
Dwork, C. (2008) · 2008
Cited alongside, same era.
Efficient, differentially private point estimators
Smith, A. D. (2008) · 2008
Cited alongside, same era.
Introduction to Nonparametric Estimation
Tsybakov, A. B. (2008) · 2008
Cited alongside, same era.
Privacy-preserving data publishing
Chen, B.-C., Kifer, D., LeFevre, K., and Machanavajjhala, A. (2009) · 2009
Cited alongside, same era.
Privacy-preserving backpropagation neural network learning
Chen, T. and Zhong, S. (2009) · 2009
Cited alongside, same era.
Secure multiterminal source coding with side information at the eavesdropper
Villard, J. and Piantanida, P. (2013) · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Bassily, R., Smith, A., and Thakurta, A. (2014) · 2014
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Privacy aware learning
Duchi, J. C., Jordan, M. I., and Wainwright, M. J. (2014) · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Privacy preserving back-propagation neural network learning made practical with cloud computing
Yuan, J. and Yu, S. (2014) · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V. S. (2015) · 2015
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Evidence and a computational explanation of cultural differences in facial expression recognition
Dailey, M. N., Joyce, C., Lyons, M. J., Kamachi, M., Ishi, H., Gyoba, J., and Cottrell, G. W. (2010) · 2010
Cited alongside, same era.
A statistical framework for differential privacy
Wasserman, L. and Zhou, S. (2010) · 2010
Cited alongside, same era.
Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D. (2011) · 2011
Cited alongside, same era.
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, B., Mironov, I., Talwar, K., and Zhang, L. (2016) · 2016
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Modeling stylized character expressions via deep learning
Aneja, D., Colburn, A., Faigin, G., Shapiro, L., and Mones, B. (2016) · 2016
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Learning with differential privacy: Stability, learnability and the sufficiency and necessity of erm principle
Wang, Y.-X., Lei, J., and Fienberg, S. E. (2016) · 2016
Later among the works it cites.