42 citations · 56 across the 4 of their papers we have counts for
6 papers
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…
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…
e-PoS: Making Proof-of-Stake Decentralized and Fair
Muhammad Saad, Zhan Qin, Kui Ren +2
Blockchain applications that rely on the Proof-of-Work (PoW) have increasingly become energy inefficient with a staggering carbon footprint. In contrast, energy-efficient alternati…
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
Yaliang Li, Houping Xiao, Zhan Qin +5
Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significan…
Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy
Haipei Sun, Boxiang Dong, Hui +3
Crowdsourcing has arisen as a new problem-solving paradigm for tasks that are difficult for computers but easy for humans. However, since the answers collected from the recruited p…