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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…