4 papers
Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +2
Geometric Data Perturbation (GDP) enables one-shot, privacy-preserving collaborative learning: each participant applies a distance-preserving transformation to its private data and…
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +1
Data Collaboration (DC) enables multiple parties to jointly train a model by sharing only linear projections of their private datasets. The core challenge in DC is to align the bas…
DC Algorithm for Estimation of Sparse Gaussian Graphical Models
Tomokaze Shiratori, Yuichi Takano
Sparse estimation for Gaussian graphical models is a crucial technique for making the relationships among numerous observed variables more interpretable and quantifiable. Various m…
New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysis
Yuta Kawakami, Yuichi Takano, Akira Imakura
In recent years, the accumulation of data across various institutions has garnered attention for the technology of confidential data analysis, which improves analytical accuracy by…