6 citations · 13 across the 5 of their papers we have counts for
5 papers
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning
Yan Kang, Hanlin Gu, Xingxing Tang +7
Conventionally, federated learning aims to optimize a single objective, typically the utility. However, for a federated learning system to be trustworthy, it needs to simultaneousl…
SPECWANDS: An Efficient Priority-based Scheduler Against Speculation Contention Attacks
Bowen Tang, Chenggang Wu, Pen-Chung Yew +7
Transient Execution Attacks (TEAs) have gradually become a major security threat to modern high-performance processors. They exploit the vulnerability of speculative execution to i…
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang +2
Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance. Concerns about t…
Khaos: The Impact of Inter-procedural Code Obfuscation on Binary Diffing Techniques
Peihua Zhang, Chenggang Wu, Mingfan Peng +6
Software obfuscation techniques can prevent binary diffing techniques from locating vulnerable code by obfuscating the third-party code, to achieve the purpose of protecting embedd…
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods
Lichen Jia, Bowen Tang, Chenggang Wu +6
The binary code similarity detection (BCSD) method measures the similarity of two binary executable codes. Recently, the learning-based BCSD methods have achieved great success, ou…