Dr Yang Liu

Senior Lecturer

Computer Science

yang.liu@swansea.ac.uk

Available For Postgraduate Supervision

Research Links

https://orcid.org/0000-0003-2486-5765

Google Scholar

About

Dr. Yang Liu received his D.Phil. in Computer Science from the University of Oxford in July 2018. He is currently a Senior Lecturer in the Department of Computer Science at Swansea University. Before joining Swansea University, he served as an Assistant Professor at the Harbin Institute of Technology in Shenzhen from 2018 to 2024.

His research interests focus on data security and privacy-preserving computing. He has expertise in federated learning, blockchain applications, and the design of privacy protection mechanisms. His current work explores the convergence of artificial intelligence and cybersecurity technologies, aiming to enhance the robustness and security of intelligent systems.

Areas Of Expertise

Security and Privacy
Federated Learning
Blockchain

Publications

Research Highlights

Liu, Q., Yang, L., Liu, Y., Deng, J., & Wu, G. (2024). Privacy-Preserving Recommendation Based on a Shuffled Federated Graph Neural Network. IEEE Internet Computing, 28(3), 17-24.
https://doi.org/10.1109/mic.2023.3349288, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67201
Jiang, Z., Xie, M., Chen, H., Pan, Y., Lyu, J., Au, M., Fang, J., Liu, Y., & Wang, X. (2024). RPSC: Regulatable Privacy-Preserving Smart Contracts on Account-Based Blockchain. IEEE Transactions on Network Science and Engineering, 11(5), 4822-4835.
https://doi.org/10.1109/tnse.2024.3421539, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67202
Liu, Y., Zhang, Y., Lin, Z., Jiang, Z., & Wang, X. (2024). The Problem of Node Records Forgery in Ethereum Network. In Mechanisms and Machine Science (pp. 931-942). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-44947-5_71, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67203
Gao, C., Li, Y., Qi, S., Liu, Y., Wang, X., Zheng, Z., & Liao, Q. (2023). Listening to Users' Voice: Automatic Summarization of Helpful App Reviews. IEEE Transactions on Reliability, 72(4), 1619-1631.
https://doi.org/10.1109/tr.2022.3217566, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67204
Gheisari, M., Ebrahimzadeh, F., Rahimi, M., Moazzamigodarzi, M., Liu, Y., Pramanik, P., Heravi, M., Mehbodniya, A., Ghaderzadeh, M., Feylizadeh, M., & Kosari, S. (2023). Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey. CAAI Transactions on Intelligence Technology, 8(3), 581-606.
https://doi.org/10.1049/cit2.12180, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67387
Parida, P., Pradhan, C., Alzubi, J., Javadpour, A., Gheisari, M., Liu, Y., & Lee, C. (2023). Elliptic curve cryptographic image encryption using Henon map and Hopfield chaotic neural network. Multimedia Tools and Applications, 82(22), 33637-33662.
https://doi.org/10.1007/s11042-023-14607-7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67386
Liu, Y., Lin, L., Jiang, L., Zhang, W., Wang, X., Gheisari, M., Gong, T., Gao, C., & Najafabadi, H. (2023). A blockchain‐based privacy‐preserving advertising attribution architecture: Requirements, design, and a prototype implementation. Software: Practice and Experience, 53(8), 1700-1721.
https://doi.org/10.1002/spe.3209, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67206
Kong, L., Luo, W., Zhang, H., Liu, Y., & Shi, Y. (2023). Evolutionary Multilabel Adversarial Examples: An Effective Black-Box Attack. IEEE Transactions on Artificial Intelligence, 4(3), 562-572.
https://doi.org/10.1109/tai.2022.3198629, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67208
Zhang, H., Zhang, W., Feng, Y., & Liu, Y. (2023). SVScanner: Detecting smart contract vulnerabilities via deep semantic extraction. Journal of Information Security and Applications, 75, 103484
https://doi.org/10.1016/j.jisa.2023.103484, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67209
Wu, K., Wang, L., Liu, B., Liu, Y., Wang, Y., & Li, J. (2023). PSPGO: Cross-Species Heterogeneous Network Propagation for Protein Function Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(3), 1713-1724.
https://doi.org/10.1109/tcbb.2022.3215257, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67210

All Research

Journal Articles

Ma, X., Ma, G., Liu, Y., & Qi, S. (2024). APCSMA: Adaptive Personalized Client-Selection and Model-Aggregation Algorithm for Federated Learning in Edge Computing Scenarios. Entropy, 26(8), 712
https://doi.org/10.3390/e26080712, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67498
Jiang, Z., Xie, M., Chen, H., Pan, Y., Lyu, J., Au, M., Fang, J., Liu, Y., & Wang, X. (2024). RPSC: Regulatable Privacy-Preserving Smart Contracts on Account-Based Blockchain. IEEE Transactions on Network Science and Engineering, 11(5), 4822-4835.
https://doi.org/10.1109/tnse.2024.3421539, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67202
Liu, Q., Yang, L., Liu, Y., Deng, J., & Wu, G. (2024). Privacy-Preserving Recommendation Based on a Shuffled Federated Graph Neural Network. IEEE Internet Computing, 28(3), 17-24.
https://doi.org/10.1109/mic.2023.3349288, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67201
Chen, Q., Wu, Y., Wang, X., Jiang, Z., Zhang, W., Liu, Y., & Alazab, M. (2024). A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 3309-3321.
https://doi.org/10.1109/jstars.2023.3260867, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67393
Gao, C., Li, Y., Qi, S., Liu, Y., Wang, X., Zheng, Z., & Liao, Q. (2023). Listening to Users' Voice: Automatic Summarization of Helpful App Reviews. IEEE Transactions on Reliability, 72(4), 1619-1631.
https://doi.org/10.1109/tr.2022.3217566, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67204
Lee, C., Gheisari, M., Shayegan, M., Ahvanooey, M., & Liu, Y. (2023). Privacy-Preserving Techniques in Cloud/Fog and Internet of Things. Cryptography, 7(4), 51
https://doi.org/10.3390/cryptography7040051, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67205
Gheisari, M., Ebrahimzadeh, F., Rahimi, M., Moazzamigodarzi, M., Liu, Y., Pramanik, P., Heravi, M., Mehbodniya, A., Ghaderzadeh, M., Feylizadeh, M., & Kosari, S. (2023). Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey. CAAI Transactions on Intelligence Technology, 8(3), 581-606.
https://doi.org/10.1049/cit2.12180, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67387
Parida, P., Pradhan, C., Alzubi, J., Javadpour, A., Gheisari, M., Liu, Y., & Lee, C. (2023). Elliptic curve cryptographic image encryption using Henon map and Hopfield chaotic neural network. Multimedia Tools and Applications, 82(22), 33637-33662.
https://doi.org/10.1007/s11042-023-14607-7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67386
Liu, Y., Lin, L., Jiang, L., Zhang, W., Wang, X., Gheisari, M., Gong, T., Gao, C., & Najafabadi, H. (2023). A blockchain‐based privacy‐preserving advertising attribution architecture: Requirements, design, and a prototype implementation. Software: Practice and Experience, 53(8), 1700-1721.
https://doi.org/10.1002/spe.3209, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67206
Kamalov, F., Gheisari, M., Liu, Y., Feylizadeh, M., & Moussa, S. (2023). Critical Controlling for the Network Security and Privacy Based on Blockchain Technology: A Fuzzy DEMATEL Approach. Sustainability, 15(13), 10068
https://doi.org/10.3390/su151310068, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67207
Kong, L., Luo, W., Zhang, H., Liu, Y., & Shi, Y. (2023). Evolutionary Multilabel Adversarial Examples: An Effective Black-Box Attack. IEEE Transactions on Artificial Intelligence, 4(3), 562-572.
https://doi.org/10.1109/tai.2022.3198629, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67208
Zhang, H., Zhang, W., Feng, Y., & Liu, Y. (2023). SVScanner: Detecting smart contract vulnerabilities via deep semantic extraction. Journal of Information Security and Applications, 75, 103484
https://doi.org/10.1016/j.jisa.2023.103484, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67209
Wu, K., Wang, L., Liu, B., Liu, Y., Wang, Y., & Li, J. (2023). PSPGO: Cross-Species Heterogeneous Network Propagation for Protein Function Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(3), 1713-1724.
https://doi.org/10.1109/tcbb.2022.3215257, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67210
Gheisari, M., Javadpour, A., Gao, J., Abbasi, A., Pham, Q., & Liu, Y. (2023). PPDMIT: a lightweight architecture for privacy-preserving data aggregation in the Internet of Things. Journal of Ambient Intelligence and Humanized Computing, 14(5), 5211-5223.
https://doi.org/10.1007/s12652-022-03866-1, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67402
Liu, Y., Xu, W., Lai, J., & Wang, J. (2023). User-Centric Federated Matrix Factorization Based on Differential Privacy. IEEE Internet Computing, 27(3), 21-27.
https://doi.org/10.1109/mic.2023.3263896, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67212
Kamila, N., Pani, S., Das, R., Bharti, P., Najafabadi, H., Gheisari, M., & Liu, Y. (2023). A near-optimal & load balanced resilient system design for high-performance computing platform. Cluster Computing, 26(2), 1535-1550.
https://doi.org/10.1007/s10586-022-03913-8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67391
Tian, Y., Zhang, W., Simpson, A., Liu, Y., & Jiang, Z. (2023). Defending Against Data Poisoning Attacks: From Distributed Learning to Federated Learning. The Computer Journal, 66(3), 711-726.
https://doi.org/10.1093/comjnl/bxab192, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67401
Gheisari, M., Shojaeian, E., Javadpour, A., Jalili, A., Esmaeili-Najafabadi, H., Bigham, B., Vorobeva, A., Liu, Y., & Rezaei, M. (2023). An Agile Privacy-Preservation Solution for IoT-Based Smart City Using Different Distributions. IEEE Open Journal of Vehicular Technology, 4, 356-362.
https://doi.org/10.1109/ojvt.2023.3243226, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67394
Kamalov, F., Pourghebleh, B., Gheisari, M., Liu, Y., & Moussa, S. (2023). Internet of Medical Things Privacy and Security: Challenges, Solutions, and Future Trends from a New Perspective. Sustainability, 15(4), 3317
https://doi.org/10.3390/su15043317, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67388
Zhu, Y., Zhou, Y., Liu, Y., Wang, X., & Li, J. (2023). SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network. Bioinformatics, 39(2)
https://doi.org/10.1093/bioinformatics/btad015, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67389
Liu, Y., Luo, J., Yang, Y., Wang, X., Gheisari, M., & Luo, F. (2023). ShrewdAttack: Low Cost High Accuracy Model Extraction. Entropy, 25(2), 282
https://doi.org/10.3390/e25020282, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67390
Liu, Y., Zhang, Y., Lin, Z., Wang, Z., & Wang, X. (2022). Simulation Method for Blockchain Systems with a Public Chain. Sensors, 22(24), 9750
https://doi.org/10.3390/s22249750, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67395
Qiao, Y., Zhang, W., Tian, Z., Yang, L., Liu, Y., & Alazab, M. (2022). Adversarial ELF Malware Detection Method Using Model Interpretation. IEEE Transactions on Industrial Informatics, 19(1), 605-615.
https://doi.org/10.1109/tii.2022.3192901, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67392
Qiao, Y., Zhang, W., Tian, Z., Yang, L., Liu, Y., & Alazab, M. (2022). Adversarial malware sample generation method based on the prototype of deep learning detector. Computers & Security, 119, 102762
https://doi.org/10.1016/j.cose.2022.102762, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67396
Gheisari, M., Yaraziz, M., Alzubi, J., Fernández-Campusano, C., Feylizadeh, M., Pirasteh, S., Abbasi, A., Liu, Y., & Lee, C. (2022). An efficient cluster head selection for wireless sensor network-based smart agriculture systems. Computers and Electronics in Agriculture, 198, 107105
https://doi.org/10.1016/j.compag.2022.107105, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67397
Wang, H., Zhang, W., He, H., Liu, P., Luo, D., Liu, Y., Jiang, J., Li, Y., Zhang, X., Liu, W., Zhang, R., & Lan, X. (2021). An Evolutionary Study of IoT Malware. IEEE Internet of Things Journal, 8(20), 15422-15440.
https://doi.org/10.1109/jiot.2021.3063840, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67400
Li, Z., Tian, Y., Zhang, W., Liao, Q., Liu, Y., Du, X., & Guizani, M. (2021). RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything. IEEE Consumer Electronics Magazine, 10(5), 93-101.
https://doi.org/10.1109/mce.2021.3059958, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67399
Liu, Y. & Simpson, A. (2017). AdSelector: A Privacy-Preserving Advertisement Selection Mechanism for Mobile Devices. The Computer Journal, 60(8), 1251-1270.
https://doi.org/10.1093/comjnl/bxx029, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67215
Liu, Y. & Simpson, A. (2016). Privacy‐preserving targeted mobile advertising: requirements, design and a prototype implementation. Software: Practice and Experience, 46(12), 1657-1684.
https://doi.org/10.1002/spe.2403, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67214

Conference Contributions

Liu, Y., Zhang, Y., Lin, Z., Jiang, Z., & Wang, X. (2024). The Problem of Node Records Forgery in Ethereum Network. In Mechanisms and Machine Science (pp. 931-942). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-44947-5_71, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67203
Liu, Y. & Simpson, A. (2020). On the Trade-Off Between Privacy and Utility in Mobile Services: A Qualitative Study. In Lecture Notes in Computer Science (pp. 261-278). Springer International Publishing.
https://doi.org/10.1007/978-3-030-42048-2_17, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67398
Liu, Y. & Simpson, A. (2016). Privacy-Preserving Targeted Mobile Advertising: Formal Models and Analysis. In Lecture Notes in Computer Science (pp. 94-110). Springer International Publishing.
https://doi.org/10.1007/978-3-319-47072-6_7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67213

Taught Modules

CSCM88 Network and Wireless Security

Low cost networked computers add eyes and ears (or sensors) and arms, legs and voices (or actuators) to the Internet ¿ called the Internet of Things (IoT) connected smart objects. Networking technologies play a critical role in almost all modern software-based systems, whether the fixed networks of computers, or the growing pervasive devices which have increasingly diverse profiles of network connectivity. As a result, they provide a potential vector for many forms of attack and are an ideal location for many threat mitigations and isolation technologies.
CSMM88 Network Security

Low cost networked computers add eyes and ears (or sensors) and arms, legs and voices (or actuators) to the Internet ¿ called the Internet of Things (IoT) connected smart objects. Networking technologies play a critical role in almost all modern software-based systems, whether the fixed networks of computers, or the growing pervasive devices which have increasingly diverse profiles of network connectivity. As a result, they provide a potential vector for many forms of attack and are an ideal location for many threat mitigations and isolation technologies.