11 citations · 31 across the 14 of their papers we have counts for
5 papers · 1 filter
Shuffling-Aware Optimization for Private Vector Mean Estimation
Shun Takagi, Seng Pei Liew
We study -dimensional unbiased mean estimation in the single-message shuffle model, where each user sends a single privatized message and the analyzer only observes the shuffled…
Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
Shun Takagi, Satoshi Hasegawa
In cross-device private federated learning, differentially private follow-the-regularized-leader (DP-FTRL) has emerged as a promising privacy-preserving method. However, existing a…
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
Fumiyuki Kato, Li Xiong, Shun Takagi +2
Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy. Most DP-FL approaches ensure DP a…
Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation
Tsubasa Takahashi, Shun Takagi, Hajime Ono +1
This paper studies how to learn variational autoencoders with a variety of divergences under differential privacy constraints. We often build a VAE with an appropriate prior distri…
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
Shun Takagi, Tsubasa Takahashi, Yang Cao +1
How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an…