7 citations · 8 across the 2 of their papers we have counts for
3 papers
cs.DB2022★ 7 cited
HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data
Fumiyuki Kato, Tsubasa Takahashi, Shun Takagi +3
How can we explore the unknown properties of high-dimensional sensitive relational data while preserving privacy? We study how to construct an explorable privacy-preserving materia…
cs.CR2021★ 1 cited
Understanding the Interplay between Privacy and Robustness in Federated Learning
Yaowei Han, Yang Cao, Masatoshi Yoshikawa
Federated Learning (FL) is emerging as a promising paradigm of privacy-preserving machine learning, which trains an algorithm across multiple clients without exchanging their data…
cs.CR2021
Preventing Manipulation Attack in Local Differential Privacy using Verifiable Randomization Mechanism
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
Several randomization mechanisms for local differential privacy (LDP) (e.g., randomized response) are well-studied to improve the utility. However, recent studies show that LDP is…