133 citations · 187 across the 13 of their papers we have counts for
13 papers
Why Is Public Pretraining Necessary for Private Model Training?
Arun Ganesh, Mahdi Haghifam, Milad Nasr +5
In the privacy-utility tradeoff of a model trained on benchmark language and vision tasks, remarkable improvements have been widely reported with the use of pretraining on publicly…
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
Banghua Zhu, Lun Wang, Qi Pang +4
We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence a…
FED-: Privacy Preserving Federated Correlation Test
Lun Wang, Qi Pang, Shuai Wang +1
In this paper, we propose the first secure federated -test protocol Fed-. To minimize both the privacy leakage and the communication cost, we recast -test to the sec…
Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space
Lun Wang, Iosif Pinelis, Dawn Song
We prove that sketch, a well-celebrated streaming algorithm for frequency moments estimation, is differentially private as is when . sketc…
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning
Lun Wang, Zaynah Javed, Xian Wu +3
Recent research has confirmed the feasibility of backdoor attacks in deep reinforcement learning (RL) systems. However, the existing attacks require the ability to arbitrarily modi…
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
Wenxiao Wang, Tianhao Wang, Lun Wang +4
Deep learning techniques have achieved remarkable performance in wide-ranging tasks. However, when trained on privacy-sensitive datasets, the model parameters may expose private in…