Masatoshi Yoshikawa

Understanding the Interplay between Privacy and Robustness in Federated Learning

2021 · arXiv (Cornell University)

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 samples. Recent works highlighted several privacy and robustness weaknesses in FL …

Locally Private Streaming Data Release with Shuffling and Subsampling

2023

Longitudinal data collection is an important task for real-time data analysts in this Big Data Era. However, the continual observation of raw data may leak user’s sensitive information. Local differential privacy is a rigorous privacy-preserving …