no code implementations • 5 Jul 2023 • Yuzheng Hu, Fan Wu, Qinbin Li, Yunhui Long, Gonzalo Munilla Garrido, Chang Ge, Bolin Ding, David Forsyth, Bo Li, Dawn Song
As the prevalence of data analysis grows, safeguarding data privacy has become a paramount concern.
no code implementations • 8 Sep 2022 • Chulin Xie, Yunhui Long, Pin-Yu Chen, Qinbin Li, Arash Nourian, Sanmi Koyejo, Bo Li
We then provide two robustness certification criteria: certified prediction and certified attack inefficacy for DPFL on both user and instance levels.
no code implementations • 8 Sep 2022 • Chulin Xie, Zhong Cao, Yunhui Long, Diange Yang, Ding Zhao, Bo Li
However, training AVs usually requires a large amount of training data collected from different driving environments (e. g., cities) as well as different types of personal information (e. g., working hours and routes).
1 code implementation • 25 Jul 2022 • Zhuowen Yuan, Fan Wu, Yunhui Long, Chaowei Xiao, Bo Li
We first explore different statistical information which can discriminate the private training distribution from other distributions.
no code implementations • 29 Sep 2021 • Chulin Xie, Yunhui Long, Pin-Yu Chen, Krishnaram Kenthapadi, Bo Li
Federated learning (FL) provides an efficient training paradigm to jointly train a global model leveraging data from distributed users.
no code implementations • 14 Aug 2021 • Fan Wu, Yunhui Long, Ce Zhang, Bo Li
We show that these DP GCN mechanisms are not always resilient against LinkTeller empirically under mild privacy guarantees ($\varepsilon>5$).
2 code implementations • 20 Mar 2021 • Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, Bo Li
In this paper, we aim to explore the power of generative models and gradient sparsity, and propose a scalable privacy-preserving generative model DATALENS.
no code implementations • 25 Sep 2019 • Yunhui Long, Suxin Lin, Zhuolin Yang, Carl A. Gunter, Han Liu, Bo Li
We present a novel approach named G-PATE for training differentially private data generator.
2 code implementations • NeurIPS 2021 • Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, Bo Li
In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator.
1 code implementation • 13 Feb 2018 • Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiao-Feng Wang, Haixu Tang, Carl A. Gunter, Kai Chen
Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model.
no code implementations • 24 Jan 2018 • Xuejing Yuan, Yuxuan Chen, Yue Zhao, Yunhui Long, Xiaokang Liu, Kai Chen, Shengzhi Zhang, Heqing Huang, Xiao-Feng Wang, Carl A. Gunter
For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1