386 citations · 862 across the 22 of their papers we have counts for
22 papers
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
Fumiyuki Kato, Li Xiong, Shun Takagi +2
Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy. Most DP-FL approaches ensure DP a…
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model
Yixuan Liu, Suyun Zhao, Li Xiong +2
Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users' attitudes need to be satisfie…
L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response
Han Wang, Hanbin Hong, Li Xiong +2
Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy conc…
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Rongmei Lin, Yonghui Xiao, Tien-Ju Yang +4
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been w…
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks
Haowen Lin, Jian Lou, Li Xiong +1
Adversarial data examples have drawn significant attention from the machine learning and security communities. A line of work on tackling adversarial examples is certified robustne…
PANDA: Policy-aware Location Privacy for Epidemic Surveillance
Yang Cao, Shun Takagi, Yonghui Xiao +2
In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's locati…