31 citations · 76 across the 16 of their papers we have counts for
8 papers · 1 filter
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
Shuyuan Zheng, Sudong Cai, Chuan Xiao +4
In collaborative machine learning (CML), data valuation, i.e., evaluating the contribution of each client's data to the machine learning model, has become a critical task for incen…
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
Lele Zheng, Yang Cao, Renhe Jiang +4
Spatiotemporal federated learning has recently raised intensive studies due to its ability to train valuable models with only shared gradients in various location-based services. O…
Federated Graph Analytics with Differential Privacy
Shang Liu, Yang Cao, Takao Murakami +5
Collaborative graph analysis across multiple institutions is becoming increasingly popular. Realistic examples include social network analysis across various social platforms, fina…
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…
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…
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…