Lingjuan Lyu
Lingjuan Lyu
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Cited by
PPFA: Privacy preserving fog-enabled aggregation in smart grid
L Lyu, K Nandakumar, B Rubinstein, J Jin, J Bedo, M Palaniswami
IEEE Transactions on Industrial Informatics 14 (8), 3733-3744, 2018
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
YL Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li ...
IEEE Internet of Things Journal, 2020
Fog-empowered anomaly detection in IoT using hyperellipsoidal clustering
L Lyu, J Jin, S Rajasegarar, X He, M Palaniswami
IEEE Internet of Things Journal 4 (5), 1174-1184, 2017
Threats to federated learning: A survey
L Lyu, H Yu, J Zhao, Q Yang
arXiv preprint arXiv:2003.02133, 2020
Towards fair and privacy-preserving federated deep models
L Lyu, J Yu, K Nandakumar, Y Li, X Ma, J Jin, H Yu, KS Ng
IEEE Transactions on Parallel and Distributed Systems 31 (11), 2524-2541, 2020
Fog-embedded deep learning for the internet of things
L Lyu, JC Bezdek, X He, J Jin
IEEE Transactions on Industrial Informatics 15 (7), 4206-4215, 2019
Privacy-preserving collaborative deep learning with application to human activity recognition
L Lyu, X He, YW Law, M Palaniswami
Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017
Local differential privacy based federated learning for Internet of Things
Y Zhao, J Zhao, M Yang, T Wang, N Wang, L Lyu, D Niyato, KY Lam
IEEE Internet of Things Journal, 2020
Privacy-preserving collaborative fuzzy clustering
L Lyu, JC Bezdek, YW Law, X He, M Palaniswami
Data & Knowledge Engineering 116, 21-41, 2018
Local differential privacy and its applications: A comprehensive survey
M Yang, L Lyu, J Zhao, T Zhu, KY Lam
arXiv preprint arXiv:2008.03686, 2020
Differentially Private Knowledge Distillation for Mobile Analytics
L Lyu, CH Chen
Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020
An improved scheme for privacy-preserving collaborative anomaly detection
L Lyu, YW Law, SM Erfani, C Leckie, M Palaniswami
2016 IEEE International Conference on Pervasive Computing and Communication …, 2016
Privacy-preserving aggregation of smart metering via transformation and encryption
L Lyu, YW Law, J Jin, M Palaniswami
2017 IEEE Trustcom/BigDataSE/ICESS, 472-479, 2017
Towards differentially private text representations
L Lyu, Y Li, X He, T Xiao
Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020
FORESEEN: towards differentially private deep inference for intelligent Internet of Things
L Lyu, JC Bezdek, J Jin, Y Yang
IEEE Journal on Selected Areas in Communications 38 (10), 2418-2429, 2020
Federated model distillation with noise-free differential privacy
L Sun, L Lyu
IJCAI’21, 2020
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
L Lyu, Y Li, K Nandakumar, J Yu, X Ma
IEEE Transactions on Dependable and Secure Computing, 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses
L Lyu, H Yu, X Ma, L Sun, J Zhao, Q Yang, PS Yu
arXiv preprint arXiv:2012.06337, 2020
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
L Lyu, X He, Y Li
EMNLP, 2020
Distributed privacy-preserving prediction
L Lyu, YW Law, KS Ng, S Xue, JYMLL Zhao
SMC‘20, 2020
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