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Found 6 papers, 2 papers with code
A compressive multi-kernel method for privacy-preserving machine learning
Compressive Privacy is a privacy framework that employs utility-preserving lossy-encoding scheme to protect the privacy of the data, while multi-kernel method is a kernel based machine learning regime that explores the idea of using multiple kernels for building better predictors.
Supervising Nyström Methods via Negative Margin Support Vector Selection
They approximate explicit, low-dimensional feature mappings for kernel functions from the pairwise comparisons with the training data.
A Differential Privacy Mechanism Design Under Matrix-Valued Query
noise to each element of the matrix, this method is often sub-optimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis.
MVG Mechanism: Differential Privacy under Matrix-Valued Query
To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves $(\epsilon,\delta)$-differential privacy.
Coupling Random Orthonormal Projection with Gaussian Generative Model for Non-Interactive Private Data Release
A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data.
Cryptography and Security
Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning
The quest for better data analysis and artificial intelligence has lead to more and more data being collected and stored.
