35 citations · 37 across the 6 of their papers we have counts for
6 papers
A compressive multi-kernel method for privacy-preserving machine learning
Thee Chanyaswad, J. Morris Chang, S. Y. Kung
As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserv…
Supervising Nyström Methods via Negative Margin Support Vector Selection
Mert Al, Thee Chanyaswad, Sun-Yuan Kung
The Nyström methods have been popular techniques for scalable kernel based learning. They approximate explicit, low-dimensional feature mappings for kernel functions from the pairw…
A Differential Privacy Mechanism Design Under Matrix-Valued Query
Thee Chanyaswad, Alex Dytso, H. Vincent Poor +1
Traditionally, differential privacy mechanism design has been tailored for a scalar-valued query function. Although many mechanisms such as the Laplace and Gaussian mechanisms can…
MVG Mechanism: Differential Privacy under Matrix-Valued Query
Thee Chanyaswad, Alex Dytso, H. Vincent Poor +1
Differential privacy mechanism design has traditionally been tailored for a scalar-valued query function. Although many mechanisms such as the Laplace and Gaussian mechanisms can b…
RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release
Thee Chanyaswad, Changchang Liu, Prateek Mittal
A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the…
Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning
Artur Filipowicz, Thee Chanyaswad, S. Y. Kung
The quest for better data analysis and artificial intelligence has lead to more and more data being collected and stored. As a consequence, more data are exposed to malicious entit…