2025

• Sun, J., Xu, G., Yang, Y., Yang, X., Li, X., Wu, C., Liu, Z., Yang, G., & Deng, R. H. (2025). Forward-secure hierarchical delegable signature for smart homes. IEEE Transactions on Information Forensics and Security, 20, 3950–3965. https://doi.org/10.1109/TIFS.2025.3555185
• Wang, D., Cao, Y., Lam, K.-Y., Chi, C. H., & Choo, K. K. R. (2025, January 28). An accountable GAKA protocol with changeable thresholds and verifiable shares in UAVs-assisted IoVs for emergency rescue. IEEE Transactions on Intelligent Transportation Systems, 26(4), 5522–5537. https://doi.org/10.1109/TITS.2025.3531383
• Jiang, Y., Shen, J., Liu, Z., Tan, C. W., & Lam, K.-Y. (2025, January 24). Towards efficient and certified recovery from poisoning attacks in federated learning. IEEE Transactions on Information Forensics & Security. https://doi.org/10.1109/TIFS.2025.3533907
• Tu, R., Kang, X., Li, E., Tan, C. W., Chi, C. H., & Lam, K.-Y. (2025). Sentences based adversarial attack on AI-generated text detectors. IEEE Transactions on Big Data. https://doi.org/10.1109/TBDATA.2025.3600034
• Du, H., Niyato, D., Kang, J., Xiong, Z., Lam, K.-Y., Fang, Y., & Li, Y. (2025). Spear or shield: The role of generative AI in intelligent network services. IEEE Network Magazine. https://doi.org/10.1109/MNET.2025.3594769
• Li, F., Chen, X., Lam, K.-Y., Wang, L., & Liu, X. (2025). A secure wireless traffic prediction with federated learning. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2025.3594326
• Li, F., Wang, Y., Lam, K.-Y., Shen, B., & Wang, L. (2025, September). A secure dynamic spectrum access scheme for Internet-of-Things with swarm learning. IEEE Internet of Things Journal, 12(18), 37720–37733. https://doi.org/10.1109/JIOT.2025.3583710
• Karunanayake, B., Khalil, I., Yi, X., & Lam, K.-Y. (2025, September). Toward LLM-driven adaptive policy orchestration for host-based intrusion detection systems in IoT environments. IEEE Network Magazine, 39(5), 66–73. https://doi.org/10.1109/MNET.2025.3579532
• Shen, B., Lam, K.-Y., Li, F., & Wang, L. (2025). Privacy-aware spectrum pricing and power control optimization for LEO satellite Internet-of-Things. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2025.3574172
• Hou, Y., Cao, Y., Xiong, H., He, D., Chi, C. H., & Lam, K.-Y. (2025). Heterogeneous parallel key-insulated multi-receiver signcryption scheme for IoV. IEEE Transactions on Information Forensics & Security. https://doi.org/10.1109/TIFS.2025.3567917
• Li, F., Yang, J., Lam, K.-Y., Shen, B., & Luo, H. (2025, July). A dynamic spectrum access scheme for Internet of Things with improved federated learning. Journal of Network and Computer Applications, 239, 104189. https://doi.org/10.1016/j.jnca.2025.104189
• Shang, C., Cao, J., Li, Z., Niu, B., Lam, K.-Y., Chi, C. H., & Li, H. (2025, August). A hierarchical encrypted compression scheme for intra-vehicle network. IEEE Transactions on Intelligent Transportation Systems, 26(8), 11916–11930. https://doi.org/10.1109/TITS.2025.3558192
• Liu, Z., Ye, H., Jiang, Y., Shen, J., Guo, J., Tjuawinata, I., & Lam, K.-Y. (2025). Privacy-preserving federated unlearning with certified client removal. IEEE Transactions on Information Forensics & Security, 20, 3966–3978. https://doi.org/10.1109/TIFS.2025.3555868
• Ye, H., Guo, J., Liu, Z., Jiang, Y., & Lam, K.-Y. (2025, April). Enhancing AI safety of machine unlearning for ensembled models. Applied Soft Computing, 174, 113011. https://doi.org/10.1016/j.asoc.2025.113011
• Liu, Z., Ye, H., Chen, C., Zheng, Y., & Lam, K.-Y. (2025). Threats, attacks, and defenses in machine unlearning: A survey. IEEE Open Journal of the Computer Society. https://doi.org/10.1109/OJCS.2025.3543483
• Tu, R., Kang, X., Tan, C. W., Chi, C. H., & Lam, K.-Y. (2025). All points guided adversarial generator for targeted attack against deep hashing retrieval. IEEE Transactions on Information Forensics & Security, 20, 1695–1709. https://doi.org/10.1109/TIFS.2025.3534585
• Fan, J., Liu, Z., Du, H., Kang, J., Niyato, D., & Lam, K.-Y. (2025, April). Improving security in IoT-based human activity recognition: A correlation-based anomaly detection approach. IEEE Internet of Things Journal, 12(7), 8301–8315. https://doi.org/10.1109/JIOT.2024.3501361
• Shang, C., Cao, J., Luo, Y., Niu, B., Gan, L., Lam, K.-Y., & Chi, C. H. (2025, March). A vision of 3GPP wireless sensing for intelligent vehicles: Scenarios, architectures, security challenges, solutions, and future trends. IEEE Network Magazine, 39(2), 80–90. https://doi.org/10.1109/MNET.2024.3480973
• He, Y., Peng, F., Cai, R., Yu, Z., Long, M., & Lam, K.-Y. (2025). Category-conditional gradient alignment for domain adaptive face anti-spoofing. IEEE Transactions on Information Forensics & Security. https://doi.org/10.1109/TIFS.2024.3486098
• Liu, Z., Jiang, Y., Jiang, W., Guo, J., Zhao, J., & Lam, K.-Y. (2025). Guaranteeing data privacy in federated unlearning with dynamic user participation. IEEE Transactions on Dependable and Secure Computing. https://doi.org/10.1109/TDSC.2024.3476533
• Liu, R., Lam, K.-Y., Zhou, W., Wu, S., Zhao, J., Hu, D., & Gong, M. (2025). STBA: Towards evaluating the robustness of DNNs for query-limited black-box scenario. IEEE Transactions on Multimedia, 27, 2666–2681. https://doi.org/10.1109/TMM.2025.3535328
• Kalapaaking, A., Khalil, I., Yi, X., Lam, K.-Y., Huang, G. B., & Wang, N. (2025, January). Auditable and verifiable federated learning based on blockchain-enabled decentralization. IEEE Transactions on Neural Networks and Learning Systems, 36(1), 102–115. https://doi.org/10.1109/TNNLS.2024.3407670
• Zheng, Y., Wang, G., Qin, J., Chen, Z., Lin, J., Wei, P., Lin, L., & Lam, K.-Y. (2025). CIREC: Causal intervention-inspired policy learning to mitigate exposure bias for interactive recommendation. IEEE Transactions on Knowledge and Data Engineering. Accepted. DOI pending

2024

1. Luo, B., Zhang, Z., Wang, Q., Ke, A., Lu, S., & He, B. (2024). AI-powered fraud detection in decentralized finance: A project life cycle perspective. ACM Computing Surveys, 57(4), Article 96, 1–38.https://doi.org/10.1145/3705296 
2. Fan, J., Liu, Z., Du, H., Kang, J., Niyato, D., & Lam, K.-Y. (2024, November 29). Improving security in IoT-based human activity recognition. IEEE Internet of Things Journal, 12(7), 8301–8315. https://doi.org/10.1109/JIOT.2024.3501361
3. Liu, Z., Jiang, Y., Shen, J., Peng, M., Lam, K.-Y., Yuan, X., & Liu, X. (2024, October 7). A survey on federated unlearning: Challenges, methods, and future directions. ACM Computing Surveys. https://doi.org/10.1145/3679014
4. Kruglik, S., Dau, S. H., Kiah, H. M., Wang, H., & Zhang, L. F. (2024, September 2). Querying twice to achieve information-theoretic verifiability in private information retrieval. IEEE Transactions on Information Forensics and Security, vol. 19, pp. 8172-8187. https://doi.org/10.1109/TIFS.2024.3453550 
5. Guo, Q., Wang, Z., Juefei-Xu, F., Lin, D., Ma, L., Feng, W., & Liu, Y. (2024, August 16). CarveNet: Carving point-block for complex 3D shape completion. IEEE Transactions on Multimedia, vol. 27, pp. 1047-1058. https://doi.org/10.1109/TMM.2024.3443613 
6. Hu, J., Zhao, Y., Tan, B. H. M., Aung, K. M. M., & Wang, H. (2024, May 17). Enabling threshold functionality for private set intersection protocols in cloud computing. IEEE Transactions on Information Forensics and Security, vol. 19, pp. 6184-6196, 2024, doi: 10.1109/TIFS.2024.3402355. https://doi.org/10.1109/TIFS.2024.3402355 
7. Liu, R., Zhou, W., Zhang, T., Chen, K., Zhao, J., & Lam, K.-Y. (2024, April 17). Boosting black-box attack to deep neural networks with conditional diffusion models. IEEE Transactions on Information Forensics & Security, 20, 1695–1709. https://doi.org/10.1109/TIFS.2024.3515792
8. Yang, M., Tjuawinata, I., Lam, K.-Y., Zhu, T., & Zhao, J. (2024, April). Local differential privacy and its applications: A comprehensive survey. IEEE Internet of Things Journal, 12(7), 8301–8315. https://doi.org/10.1109/JIOT.2024.3501361
9. Kalapaaking, A., Khalil, I., Yi, X., Lam, K.-Y., Huang, G. B., & Wang, N. (2024, June 14). Auditable and verifiable federated learning based on blockchain-enabled decentralization. IEEE Transactions on Neural Networks and Learning Systems, 36(1), 102–115. https://doi.org/10.1109/TNNLS.2024.3407670
10. Liu, Z., Guo, J., Yang, W., Fan, J., Lam, K.-Y., & Zhao, J. (2024, January 19). Dynamic user clustering for efficient and privacy-preserving federated learning. IEEE Transactions on Dependable and Secure Computing. https://doi.org/10.1109/TDSC.2024.3476533
11. Lam, K.-Y., Lu, X., Zhang, L., Wang, X., Wang, H., & Goh, S. Q. (2024, January 12). Efficient FHE-based privacy-enhanced neural network for trustworthy AI-as-a-service. IEEE Transactions on Information Forensics & Security. https://doi.org/10.1109/TIFS.2025.3567917

2023

• An Efficient FHE-Enabled Secure Cloud-Edge Computing Architecture for IoMTs Data Protection With Its Application to Pandemic Modelling — L. Zhang, X. Wang, J. Wang, R. Pung, H. Wang, Kwok-Yan Lam — 29-Dec-2023
• Survey on Digital Sovereignty and Identity: From Digitization to Digitalization — Kheng Leong Tan, Chi-Hung Chi, Kwok-Yan Lam — 5-Oct-2023
• An Advanced Integrated Visible Light Communication and Localization System — H. Yang, S. Zhang, A. Alphones, C. Chen, Kwok-Yan Lam, Z. Xiong, L. Xiao, Y. Zhang — 29-Aug-2023
• Joint Device Scheduling and Bandwidth Allocation for Federated Learning over Wireless Networks — T. Zhang, Kwok-Yan Lam, J. Zhao, J. Feng — 12-Jul-2023

2022

• Privacy-Preserving Aggregation in Federated Learning: A Survey — Ziyao Liu, J. Guo, Kwok-Yan Lam, J. Zhao — 15-Jul-2022
• Traceable Policy-Based Signatures and Instantiation from Lattices — Y. Xu, R. Safavi-Naini, K. Nguyen, H. Wang — 2022
• Bivariate Polynomial-Based Secret Sharing Schemes with Secure Secret Reconstruction — J. Ding, P. Ke, C. Lin, H. Wang — 2022
• A New Framework for Deniable Secure Key Exchange — S. Jiang, Y.M. Chee, S. Ling, H. Wang, C. Xing — 2022
• Privacy-Preserving Statistical Analysis over Multi-Dimensional Aggregated Data in Edge Computing-Based Smart Grid Systems — X. Zhang, C. Huang, G. Du, J. Zhang, J. Xue, H. Wang - 2022

2025

• Searchable Encryption for Conjunctive Queries with Extended Forward and Backward Privacy — Cong Zuo, Shangqi Lai, Shi-Feng Sun, Xingliang Yuan, Joseph K. Liu, Jun Shao, Huaxiong Wang, Liehuang Zhu, Shujie Cui — 1-Jan-2025
• Towards Harmless Rawlsian Fairness Regardless of Demographic Prior — Xuanqiang Wang, Jing Li, Ivor W. Tsang, Yew-Soon Ong — 20-Feb-2025
• THEMIS: Regulating Textual Inversion for Personalized Concept Censorship — Yutong Wu, Jie Zhang, Florian Kerschbaum, Tianwei Zhang — 23-Feb-2025

2024

• Diving Deep into the Preimage Security of AES-like Hashing — Shiyao Chen, Eik List, Jian Guo, Danping Shi, Tianyu Zhang — 29-Apr-2024
• Cross-Context Backdoor Attacks Against Graph Prompt Learning — Xiaoting Lyu, Yufei Han, Wei Wang, Hangwei Qian, Ivor Tsang, Xiangliang Zhang — 24-Aug-2024
• OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams — Yiqun Diao, Yutong Wang, Qinbin Li, Bingsheng He, Mian Lu — 30-Aug-2024
• Continual Learning Optimizations for Auto-regressive Decoder of Multilingual ASR Systems — Chin Yuen Kwok, Jia Qi Yip, Eng Siong Chng — 1-Sep-2024
• Towards Physical World Backdoor Attacks Against Skeleton Action Recognition — Qichen Zheng, Yi Yu, Siyuan Yang, Jun Liu, Kwok-Yan Lam, Alex Kot — 29-Sep-2024
• Adversarially Robust Distillation by Reducing the Student-Teacher Variance Gap — Junhao Dong, Piotr Koniusz, Junxi Chen, Yew-Soon Ong — 29-Sep-2024
• Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory — Sensen Gao, Xiaojun Jia, Xuhong Ren, Ivor Tsang, Qing Guo — 28-Sep-2024
• SAIR: Learning Semantic-aware Implicit Representation — Canyu Zhang, Xiaoguang Li, Qing Guo, Song Wang — 28-Sep-2024
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models — Kunsheng Tang, Wenbo Zhou, Jie Zhang, Aishan Liu, Gelei Deng, Shuai Li, Peigui Qi, Weiming Zhang, Tianwei Zhang, Nenghai Yu — 14-Oct-2024
• Chosen-Prefix Collisions on AES-like Hashing — Shiyao Chen, Xiaoyang Dong, Jian Guo, Tianyu Zhang — 18-Dec-2024
• Opening the Blackbox: Collision Attacks on Round-Reduced Tip5, Tip4, Tip4’ and Monolith — Fukang Liu, Katharina Koschatko, Lorenzo Grassi, Hailun Yan, Shiyao Chen, Subhadeep Banik, Willi Meier — 18-Dec-2024
• Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data — Zhaomin Wu, Junyi Hou, Yiqun Diao, Bingsheng He — 15-Dec-2024
• Improved Particle Approximation Error for Mean Field Neural Networks — Atsushi Nitanda — 15-Dec-2024
• Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning — Mengmeng Chen, Xiaohu Wu, Xiaoli Tang, Tiantian He, Yew-Soon Ong, Qiqi Liu, Qicheng Lao, Han Yu — 15-Dec-2024
• Transferable Adversarial Attacks on SAM and Its Downstream Models — Song Xia, Wenhan Yang, Yi Yu, Xun Lin, Henghui Ding, Lingyu Duan, Xudong Jiang — 15-Dec-2024
• Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization — ST Ho, T Van Vo, S Ebrahimkhani, NM Cheung — 15-Dec-2024
• FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation — CTH Teo, M Abdollahzadeh, X Ma, N Cheung — 15-Dec-2024

Books

• X. Yi. X. Yang, X. Liu, A. Kelarev, K.Y. Lam, M. Yang, X. Wang and E. Bertino. “Privacy Enhancing Techniques: Practices and Applications”, Springer Cham, July 2025, ISBN 978-3-031-95139-8. https://doi.org/10.1007/978-3-031-95140-4.

Projects and Case Studies

• Overcoming Data Barriers via Trustworthy Privacy-Enhancing Technologies  
  GPAI partnership on AI, 2023. 
  The Digital Trust Centre contributed to the GPAI Data Governance Working Group (supported by Capgemini), in collaboration with IMDA, to explore the use of privacy-enhancing technologies for AI-for-social-good. The project demonstrated how PETs can enable secure data sharing to support pandemic resilience.
  
• Digital Advertising In A Paradigm Without 3rd Party Cookies IMDA Pet Sandbox – Meta Case Study
  The Digital Trust Centre contributed to IMDA’s PET Sandbox initiative, which examined the future of digital advertising without third-party cookies. The project explored privacy-enhancing technologies and alternative solutions, with findings published in the paper Digital Advertising in a Paradigm Without 3rd Party Cookies, produced by IMDA in partnership with Meta.
  More information here