2 papers
cs.LG2022
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Ryuichi Ito, Seng Pei Liew, Tsubasa Takahashi +2
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the m…
cs.CR2021
Construction of Differentially Private Summaries over Fully Homomorphic Encryption
S. Ushiyama, T. Takahashi, M. Kudo +1
Cloud computing has garnered attention as a platform of query processing systems. However, data privacy leakage is a critical problem. Chowdhury et al. proposed Crypt(epsilon), whi…