31 citations · 76 across the 16 of their papers we have counts for
6 papers · 1 filter
Local Differential Privacy Image Generation Using Flow-based Deep Generative Models
Hisaichi Shibata, Shouhei Hanaoka, Yang Cao +5
Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To rele…
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa
The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collabora…
A Crypto-Assisted Approach for Publishing Graph Statistics with Node Local Differential Privacy
Shang Liu, Yang Cao, Takao Murakami +1
Publishing graph statistics under node differential privacy has attracted much attention since it provides a stronger privacy guarantee than edge differential privacy. Existing wor…
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Ruixuan Liu, Yanlin Wang, Yang Cao +4
Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the prob…
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
Combining Federated Learning (FL) with a Trusted Execution Environment (TEE) is a promising approach for realizing privacy-preserving FL, which has garnered significant academic at…
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release
Donghao Li, Yang Cao, Yuan Yao
Privacy-preserving data release algorithms have gained increasing attention for their ability to protect user privacy while enabling downstream machine learning tasks. However, the…