411 citations · 1.6k across the 30 of their papers we have counts for
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Privacy and Robustness in Federated Learning: Attacks and Defenses
Lingjuan Lyu, Han Yu, Xingjun Ma +5
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (…
Not Just Cloud Privacy: Protecting Client Privacy in Teacher-Student Learning
Lichao Sun, Ji Wang, Philip S. Yu +1
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One recent popular approach to study these…
Private Deep Learning with Teacher Ensembles
Lichao Sun, Yingbo Zhou, Ji Wang +4
Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information. Most pri…
Adversarial Attack and Defense on Graph Data: A Survey
Lichao Sun, Yingtong Dou, Carl Yang +5
Deep neural networks (DNNs) have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However,…