Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing.
Proc. Priv. Enhancing Technol., 2019
RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release.
Proc. Priv. Enhancing Technol., 2019
Privacy-Preserving Machine Learning via Data Compression & Differential Privacy
PhD thesis, 2018
Supervising Nyström Methods via Negative Margin Support Vector Selection.
CoRR, 2018
A Differential Privacy Mechanism Design Under Matrix-Valued Query.
CoRR, 2018
Outlier Removal for Enhancing Kernel-Based Classifier Via the Discriminant Information.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018
Multi-Kernel, Deep Neural Network and Hybrid Models for Privacy Preserving Machine Learning.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018
MVG Mechanism: Differential Privacy under Matrix-Valued Query.
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018
Collaborative PCA/DCA Learning Methods for Compressive Privacy.
ACM Trans. Embed. Comput. Syst., 2017
Coupling Dimensionality Reduction with Generative Model for Non-Interactive Private Data Release.
CoRR, 2017
Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning.
CoRR, 2017
Differential mutual information forward search for multi-kernel discriminant-component selection with an application to privacy-preserving classification.
Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017
A compressive multi-kernel method for privacy-preserving machine learning.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017
Discriminant-component eigenfaces for privacy-preserving face recognition.
Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016