no code implementations • 28 Apr 2022 • Jin Xu, Chi Hong, Jiyue Huang, Lydia Y. Chen, Jérémie Decouchant
Recent reconstruction attacks apply a gradient inversion optimization on the gradient update of a single minibatch to reconstruct the private data used by clients during training.
no code implementations • 31 Jan 2022 • Chi Hong, Jiyue Huang, Lydia Y. Chen
However, they are all based on competing generator-substitute networks and hence encounter training instability. In this paper we propose a data-free model stealing frame-work, MEGA, which is based on collaborative generator-substitute networks and only requires the target model toprovide label prediction for synthetic query examples.
no code implementations • 29 Sep 2021 • Chi Hong, Jiyue Huang, Lydia Y. Chen
Deep machine learning models are increasingly deployed in the wild, subject to adversarial attacks.
no code implementations • 20 Jun 2021 • Jiyue Huang, Chi Hong, Lydia Y. Chen, Stefanie Roos
Shapley Value is commonly adopted to measure and incentivize client participation in federated learning.
no code implementations • 13 Nov 2020 • Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.
no code implementations • 19 Jul 2018 • Chi Hong, Amirmasoud Ghiassi, Yichi Zhou, Robert Birke, Lydia Y. Chen
Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1. 5 percent points for synthetic and real-world datasets, respectively.
no code implementations • 13 Jun 2017 • Chi Hong
In this paper, we propose generative probabilistic models for label aggregation.