no code implementations • 20 Jun 2021 • Thee Chanyaswad, J. Morris Chang, S. Y. Kung
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.
no code implementations • 10 May 2018 • Mert Al, Thee Chanyaswad, Sun-Yuan Kung
They approximate explicit, low-dimensional feature mappings for kernel functions from the pairwise comparisons with the training data.
1 code implementation • 26 Feb 2018 • Thee Chanyaswad, Alex Dytso, H. Vincent Poor, Prateek Mittal
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.
no code implementations • 2 Jan 2018 • Thee Chanyaswad, Alex Dytso, H. Vincent Poor, Prateek Mittal
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.
1 code implementation • 31 Aug 2017 • 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.
Cryptography and Security
no code implementations • 24 Jul 2017 • 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.