Xingliang Yuan

Associate Professor
Cybersecurity Group
School of Computing and Information Systems
The University of Melbourne

Email: xingliang.yuan [at] unimelb.edu.au

Overview

I am an Associate Professor in the School of Computing and Information Systems at the University of Melbourne. Before that, I was a faculty member at Monash University. I have broad interests in computer security, including data security and privacy, secure networked systems, and trustworthy machine learning. My recent research focus can be found here. I am always looking for self-motivated students. Please email me your CV, transcript, research statement (no more than 200 words), and english test score. Information on UniMelb PhD admission and scholarships can be found here, here, here, here, and here (domestic only).

News

[Dec'24] Our paper "OblivCDN: A Practical Privacy-preserving CDN with Oblivious Content Access" has been accepted by ACM AsiaCCS'25 (11 straightly accepted papers out of 289 submissions in Cycle 1).

[Nov'24] I will co-organise the Second Symposium on Digital Trust at Nanyang Technological University.

[Nov'24] Our paper "SIGuard: Guarding Secure Inference with Post Data Privacy" has been accepted by NDSS'25.

[Nov'24] I will co-organise RAID'25 as a general co-chair.

[Jul'24] I am deeply honored to be awarded with an ARC Future Fellowship.

[Jun'24] I have officially joined the University of Melbourne as an Associate Professor.

[Jun'24] I am invited to serve as the TPC member for IEEE S&P'25.

[Apr'24] Our paper "OblivGNN: Oblivious Inference on Transductive and Inductive Graph Neural Network" has been accepted by USENIX Security'24.

[Apr'24] I am invited to serve as the TPC member for NDSS'25.

[Mar'24] Our paper "Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks" has been accepted by IEEE ICDE'24.

[Feb'24] Our paper "Trustworthy Graph Neural Networks: Aspects, Methods and Trends" has been accepted by Proceedings of The IEEE and been featured in the issue cover.

[Nov'23] Our paper "GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks" has been accepted by NDSS'24.

[Oct'23] Our paper "Securing Graph Neural Networks in MLaaS: A Comprehensive Realisation of Query-based Integrity Verification" has been accepted by IEEE S&P'24 (Oakland).

[Oct'23] I am invited to serve as the track co-chair (Security, Privacy, and Trust in Distributed Systems) for IEEE ICDCS'24.

Recent Publications

My full publication list can be found at DBLP and Google Scholar.

Encrypted Search

[ACM CCS'23] Lei Xu, Leqian Zheng, Chengzhi Xu, Xingliang Yuan, and Cong Wang, “Leakage-Abuse Attacks Against Forward and Backward Private Searchable Symmetric Encryption”, In the 30th ACM Conference on Computer and Communications Security , Copenhagen, Denmark, 2023 (Acceptance ratio: 19%).
[IEEE TKDE] Viet Vo, Xingliang Yuan, Shi-Feng Sun, Joseph Liu, Surya Nepal, and Cong Wang, “ShieldDB: An Encrypted Document Database with Padding Countermeasures", IEEE Transactions on Knowledge and Data Engineering, Accepted, 2021.
[ISOC NDSS'21] Shi-Feng Sun, Ron Steinfeld, Shangqi Lai, Xingliang Yuan, Amin Sakzad, Joseph Liu, Surya Nepal, and Dawu Gu, “Practical Non-Interactive Searchable Encryption with Forward and Backward Privacy”, In the Network and Distributed System Security Symposium, 2021 (Acceptance ratio: 15%).
[ACM AsiaCCS'19] Shangqi Lai, Xingliang Yuan, Shi-Feng Sun, Joseph K. Liu, Yuhong Liu, and Dongxi Liu, “GraphSE^2: An Encrypted Graph Database for Privacy-Preserving Social Search”, in the 14th ACM ASIA Conference on Computer and Communications Security , Auckland, New Zealand, 2019 (Acceptance ratio: 44/258 = 17%).
[ACM CCS'18] Shi-Feng Sun, Xingliang Yuan, Joseph K. Liu, Ron Steinfeld, Amin Sakzad, Viet Vo, and Surya Nepal, “Practical Backward-Secure Searchable Encryption from Symmetric Puncturable Encryption”, in the 25th ACM Conference on Computer and Communications Security , Toronto, Canada, 2018. (Acceptance ratio: 134/809 = 16.6%).

Secure Networked Protocols and Systems

[ACM AsiaCCS'25] Viet Vo, Shangqi Lai, Xingliang Yuan, Surya Nepal, and Qi Li, “OblivCDN: A Practical Privacy-preserving CDN with Oblivious Content Access”, In the 20th ACM ASIA Conference on Computer and Communications Security , 2025 (11 straightly accepted papers out of 289 submissions in Cycle 1).
[IEEE TDSC] Shangqi Lai, Xingliang Yuan, Shi-Feng Sun, Joseph Liu, Ron Steinfeld, Amin Sakzad, and Dongxi Liu, “Practical Encrypted Network Traffic Pattern Matching for Secure Middleboxes”, IEEE Transactions on Dependable and Secure Computing, In Press, 2021.
[ISOC NDSS'21] Shangqi Lai, Xingliang Yuan, Joseph Liu, Xun Yi, Qi Li, Dongxi Liu, and Surya Nepal, “OblivSketch: Oblivious Network Measurement as a Cloud Service”, In the Network and Distributed System Security Symposium, virtually, 2021 (Acceptance ratio: 15%).
[ACM CCS'19] Huayi Duan, Cong Wang, Xingliang Yuan, Yajin Zhou, Qian Wang, and Kui Ren, “LightBox: Full-stack Protected Stateful Middlebox at Lightning Speed”, in the 26th ACM Conference on Computer and Communications Security, , London, UK, 2019 (Acceptance ratio: 149/933 = 16%).

Privacy-Preserving Machine Learning

[ISOC NDSS'25] Xinqian Wang, Xiaoning Liu, Shangqi Lai, Xun Yi, and Xingliang Yuan, “SIGuard: Guarding Secure Inference with Post Data Privacy", in the Network and Distributed System Security Symposium, 2025.
[USENIX Security'24] Zhibo Xu, Shangqi Lai, Xiaoning Liu, Alsharif Abuadbba, Xingliang Yuan, and Xun Yi, “OblivGNN: Oblivious Inference on Transductive and Inductive Graph Neural Network", in the 33rd USENIX Security Symposium, 2024.
[IEEE INFOCOM'22] Yi Liu, Lei Xu, Xingliang Yuan, Cong Wang, and Bo Li, “The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining", in the 41st International Conference on Computer Communications, 2022 (Acceptance ratio: 19.9%).
[IEEE TIFS] Xiaoning Liu, Bang Wu, Xingliang Yuan, and Xun Yi, “Leia: A Lightweight Cryptographic Neural Network Inference System at the Edge", IEEE Transactions on Information Forensics and Security, Accepted, 2021.
[ESORICS'21] Xiaoning Liu, Yifeng Zheng, Xingliang Yuan, and Xun Yi, “MediSC: Towards Secure and Lightweight Deep Learning as a Medical Diagnostic Service”, In the 26th European Symposium on Research in Computer Security, virtually, 2021 (Best Paper Award, Acceptance ratio: 21%).

Secure and Trustworthy Graph Learning

[IEEE S&P'25] Jiadong Lou, Xu Yuan, Rui Zhang, Xingliang Yuan, Neil Gong, and Nian-Feng Tzeng, “GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models”, In IEEE Symposium on Security and Privacy, Oakland, USA, 2025.
[IEEE ICDE'24] He Zhang, Xingliang Yuan, and Shirui Pan, “Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks”, In IEEE 40th International Conference on Data Engineering , 2024.
[IEEE S&P'24] Bang Wu, Xingliang Yuan, Shuo Wang, Qi Li, Minhui Xue, and Shirui Pan, “Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification”, In IEEE Symposium on Security and Privacy, Oakland, USA, 2024.
[ISOC NDSS'24] Bang Wu, He Zhang, Xiangwen Yang, Shuo Wang, Minhui Xue, Shirui Pan, and Xingliang Yuan, “GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks”, In Network and Distributed System Security Symposium, San Diego, USA, 2024.
[Proceeding of the IEEE] He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei, “Trustworthy Graph Neural Networks: Aspects, Methods, and Trends”, In Proceedings of the IEEE, 2024.
[ICML'23] He Zhang, Bang Wu, Shuo Wang, Xiangwen Yang, Minhui Xue, Shirui Pan, and Xingliang Yuan, “Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks”, In the International Conference on Machine Learning, Honolulu, Hawaii, USA, 2023.
[ACM AsiaCCS'22] Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan, “Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realisation”, In the 17th ACM ASIA Conference on Computer and Communications Security , 2022 (First round acceptance ratio: 15%).
[IEEE ICDM'21] Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan, “Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications”, In the IEEE International Conference on Data Mining, 2021.