31 citations · 76 across the 16 of their papers we have counts for
4 papers · 1 filter
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…
FL-Market: Trading Private Models in Federated Learning
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa +2
The difficulty in acquiring a sufficient amount of training data is a major bottleneck for machine learning (ML) based data analytics. Recently, commoditizing ML models has been pr…
Pricing Private Data with Personalized Differential Privacy and Partial Arbitrage Freeness
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa
There is a growing trend regarding perceiving personal data as a commodity. Existing studies have built frameworks and theories about how to determine an arbitrage-free price of a…
Asymmetric Differential Privacy
Shun Takagi, Yang Cao, Masatoshi Yoshikawa
Differential privacy (DP) is getting attention as a privacy definition when publishing statistics of a dataset. This paper focuses on the limitation that DP inevitably causes two-s…