139 citations · 492 across the 39 of their papers we have counts for
6 papers · 2 filters
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lingjuan Lyu, Han Yu, Xingjun Ma +5
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (…
A Fast and Scalable Authentication Scheme in IoT for Smart Living
Jianhua Li, Jiong Jin, Lingjuan Lyu +4
Numerous resource-limited smart objects (SOs) such as sensors and actuators have been widely deployed in smart environments, opening new attack surfaces to intruders. The severe se…
Federated Model Distillation with Noise-Free Differential Privacy
Lichao Sun, Lingjuan Lyu
Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures. Sharing prediction instead…
Local Differential Privacy and Its Applications: A Comprehensive Survey
Mengmeng Yang, Lingjuan Lyu, Jun Zhao +2
With the fast development of Information Technology, a tremendous amount of data have been generated and collected for research and analysis purposes. As an increasing number of us…
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
Lingjuan Lyu, Yitong Li, Karthik Nandakumar +2
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens a…
Cloud-based Privacy-Preserving Collaborative Consumption for Sharing Economy
Lingjuan Lyu, Sid Chi-Kin Chau, Nan Wang +1
Cloud computing has been a dominant paradigm for a variety of information processing platforms, particularly for enabling various popular applications of sharing economy. However,…