91 citations · 146 across the 4 of their papers we have counts for
3 papers · 1 filter
On the Privacy Risks of Algorithmic Fairness
Hongyan Chang, Reza Shokri
Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair machine learning aims at minimizing discrimination against protected groups by, for exa…
On Adversarial Bias and the Robustness of Fair Machine Learning
Hongyan Chang, Ta Duy Nguyen, Sasi Kumar Murakonda +2
Optimizing prediction accuracy can come at the expense of fairness. Towards minimizing discrimination against a group, fair machine learning algorithms strive to equalize the behav…
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Hongyan Chang, Virat Shejwalkar, Reza Shokri +1
Collaborative (federated) learning enables multiple parties to train a model without sharing their private data, but through repeated sharing of the parameters of their local model…