42 citations · 96 across the 16 of their papers we have counts for
5 papers · 1 filter
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
Shuo Shao, Wenyuan Yang, Hanlin Gu +4
Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL e…
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Xiaochen Li, Yuke Hu, Weiran Liu +5
Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the…
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…
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training
Guodong Cao, Zhibo Wang, Xiaowei Dong +4
Adversarial training has been widely explored for mitigating attacks against deep models. However, most existing works are still trapped in the dilemma between higher accuracy and…
Backdoor Defense via Decoupling the Training Process
Kunzhe Huang, Yiming Li, Baoyuan Wu +2
Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few trainin…