Xiang, Yong
Deakin University
8 papers on CoChatORCID 0000-0003-3545-7863
Most cited papers
- Verified
Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing
Qu, Y., Gao, L., Luan, T. H., Xiang, Y., Yu, S., Li, B., & Zheng, G. (2020) · IEEE Internet of Things Journal
Blockchain Technology Applications and SecurityPrivacy-Preserving Technologies in Data
Cited by 451
- Verified
A dynamic automated lane change maneuver based on vehicle-to-vehicle communication
Luo, Y., Xiang, Y., Cao, K., & Li, K. (2016) · Transportation Research Part C: Emerging Technologies
Autonomous Vehicle Technology and SafetyVehicle Dynamics and Control Systems
Cited by 295
- Verified
Blockchain-enabled Federated Learning: A Survey
Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J. (2023) · ACM Computing Surveys
Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security
Cited by 286
- Verified
Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey
Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials
Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning
Cited by 152
- Verified
A compression-diffusion-permutation strategy for securing image
Huang, H., He, X., Xiang, Y., Wen, W., & Zhang, Y. (2018) · Signal Processing
Chaos-based Image/Signal EncryptionAdvanced Steganography and Watermarking Techniques
Cited by 48
- Verified
Cryptanalysis and improvement of a chaotic image encryption by first-order time-delay system
Li, M., Fan, H., Xiang, Y., Li, Y., & Zhang, Y. (2018) · IEEE MultiMedia
Chaos-based Image/Signal EncryptionComputer Science and Engineering
Cited by 47
- Verified
A technical survey on statistical modelling and design methods for crowdsourcing quality control
Jin, Y., Carman, M., Zhu, Y., & Xiang, Y. (2020) · Artificial Intelligence
Mobile Crowdsensing and CrowdsourcingMachine Learning and Data Classification
Cited by 28
- Verified
Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis
R.G., G., Sajjanhar, A., & Xiang, Y. (2024) · Expert Systems with Applications
Adversarial Robustness in Machine LearningInformation and Cyber Security
Cited by 28
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