Dr Jason Xue

Dr Jason Xue

  • Biography/ Background

    Bio Sketch:

    Minhui Xue is a (continuing) Lecturer (a.k.a. Assistant Professor) of School of Computer Science at the University of Adelaide. He is also an Honorary Lecturer with Macquarie University. Previously, he was a Research Fellow with Macquarie University and a visiting research scientist at CSIRO-Data61 at Sydney, Australia. His current research interests are machine learning security and privacy, system and software security, and Internet measurement. He is the recipient of the ACM CCS Best Paper Award Runner-Up, ACM SIGSOFT distinguished paper award, Best Student Paper Award, and the IEEE best paper award, and his work has been featured in the mainstream press, including The New York Times, Science Daily, PR Newswire, Yahoo, The Australian Financial Review, and The Courier. He co-chaired the 1st IEEE AI4MOBILE workshop and the 1st IEEE MASS workshop on Smart City Security and Privacy. He currently serves on the Program Committees of IEEE Symposium on Security and Privacy (Oakland) 2021, 2023, ACM CCS 2021, 2022, USENIX Security 2021, 2022, NDSS 2021, 2022, IEEE/ACM ICSE 2021, 2022, 2023, PETS 2021, 2022, ESORICS 2021, and ACM ASIACCS 2021. He is a member of both ACM and IEEE.

    Education:

    • PhD – Computer Science, East China Normal University, 2013 - 2018, PhD Advisor: Keith W. Ross
    • Bachelor of Science – Pure and Applied Mathematics, East China Normal University, 2009 - 2013

    Experience:

    • Lecturer, The University of Adelaide, 2019 - Present
    • Research Fellow, Macquarie University, 2018 - 2019
    • Visiting Research Scientist, CSIRO-Data61, 2018 - 2019                                                                        
    • Visiting PhD Student, Nanyang Technological University (NTU), 2017 - 2018
    • Visiting PhD Student, Katholieke Universiteit Leuven (KU Leuven), 2017                                                    
    • Visiting PhD Student, Shanghai Jiao Tong University, 2017                                                                               
    • Visiting PhD Student, Courant Institute of Mathematical Sciences, New York University, 2016 - 2017
    • Visiting Student, New York University, 2012

  • Awards & Achievements

    Awards:

    • Best Student Paper Award, Australasian Information Security Conference, 2022
    • ACM CCS Best Paper Award Runner-Up, 2021
    • ACM SIGSOFT Distinguished Paper Award, 2018                                                  
    • Research Forum Award, Deep Learning Security Workshop (NUS), 2017
    • Best Paper Award, IEEE International Symposium on Security and Privacy in Social Networks and Big Data, 2015
    • Faculty Award of Overall Awesome, The University of Adelaide, 2021

    Selected Press:

    • COVIDSafe app best of class for privacy, says study, The Australian Financial Review, July 2020
    • COVIDSafe app dubbed safest in the world, The Courier, July 2020
    • COVIDSafe app dubbed safest in the world, The Canberra Times, July 2020
    • Researchers Uncover a Flaw in Europe’s Tough Privacy Rules, The New York Times, June, 2016
    • A Loophole in the Right to Be Forgotten, Columbia Journalism Review, July 2016
    • Flaws Found in ‘Right To Be Forgotten’ Data Privacy Laws, Information Week, July 2016
    • Is Anything Ever ‘Forgotten’ Online?, The Conversation, July 2016
    • Weak Spots in Europe’s ‘Right to be Forgotten’ Data Privacy law, Science Daily, June 2016
    • NYU Researchers Find Weak Spots in Europe’s ‘Right to be Forgotten’ Data Privacy Law, NYU Newsroom, June 2016
    • Hold That Talk: NYU Researchers Discover Clues For Identifying Yik Yak Users on College Campuses, PR Newswire, ACM TechNews, Yahoo, October, 2016
    • Yik Yak Could Lose Anonymity, Washington Square News, October, 2016
    • Mining WeChat to Understand the Chinese Diaspora, NYU Center for Data Science, April, 2018 

    Grant Awards:

    • ARC Discovery Project: Intelligent Technologies for Smart Cryptography (co-CI), 2021
    • Automatic Post-Quantum Cryptographic Code Generation and Optimization, Google Research Scholar Award (co-CI), 2021
    • RBlavatnik Interdisciplinary Cyber Research Center, Tel ‎Aviv University, Israel: Leakage-free Cryptography: Eliminating Side Channel Leakage Using Compiler Optimization (co-CI), 2020
    • ECMS COVID-19 Recognition Fund, The University of Adelaide, Australia (lead-CI), 2021
    • Sustainable & Smart Built Environment ECMS Seed Funding, The University of Adelaide, Australia: Intelligent Technologies for Smart Cryptography (co-CI), 2020

  • Research Interests

    • Machine Learning Security and Privacy
    • System and Software Security
    • Internet Measurement and Fraud Detection

  • Publications

    2022 

    • Kunpeng Zhang, Xi Xiao, Xiaogang Zhu, Ruoxi Sun, Minhui Xue, and Sheng Wen, Path Transitions Tell More: Optimizing Fuzzing Schedules via Runtime Program States, ACM International Conference on Software Engineering (ICSE), 2022
    • Zirui Peng, Shaofeng Li, Guoxing Chen, Cheng Zhang, Haojin Zhu, Minhui Xue, Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations, Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Oral)
    • Hamish Spencer, Wei Wang, Ruoxi Sun, Minhui Xue, Dissecting Malware in the Wild, Australasian Information Security Conference (AISC), 2022 (Best Student Paper Award)
    • Matthew Crawford, Wei Wang, Ruoxi Sun, Minhui Xue, Statically Detecting Adversarial Malware through Randomised Chaining, Australasian Information Security Conference (AISC), 2022

    2021

    • Ruoxi Sun, Wei Wang, Minhui Xue, Gareth Tyson, Seyit Camtepe, and Damith Ranasinghe, An Empirical Assessment of Global COVID-19 Contact Tracing Applications, IEEE International Conference on Software Engineering (ICSE), 2021
    • Aoting Hu, Renjie Xie, Zhigang Lu, Aiqun Hu, and Minhui Xue, TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing, ACM Conference on Computer and Communications Security (CCS), 2021
    • Shaofeng Li, Hui Liu, Tian Dong, Benjamin Zi Hao Zhao, Minhui Xue, Haojin Zhu, and Jialiang Lu, Hidden Backdoors in Human-Centric Language Models, ACM Conference on Computer and Communications Security (CCS), 2021 (Best Paper Award Runner-Up)
    • Tong Zhu, Yan Meng, Haotian Hu, Xiaokuan Zhang, Minhui Xue, and Haojin Zhu, Dissecting Click Fraud Autonomy in the Wild, ACM Conference on Computer and Communications Security (CCS), 2021 
    • Suibin Sun, Le Yu, Xiaokuan Zhang, Minhui Xue, Ren Zhou, Haojin Zhu, Shuang Hao, and Xiaodong Lin, Understanding and Detecting Mobile Ad Fraud Through the Lens of Invalid Traffic, ACM Conference on Computer and Communications Security (CCS), 2021
    • Xiaotao Feng, Ruoxi Sun, Xiaogang Zhu, Minhui Xue, Sheng Wen, Dongxi Liu, Surya Nepal, and Yang Xiang, SNIPUZZ: Black-box Fuzzing of IoT Firmware via Message Snippet Inference, ACM Conference on Computer and Communications Security (CCS), 2021
    • Yuantian Miao, Minhui Xue, Chao Chen, Lei Pan, Jun Zhang, Benjamin Zi Hao Zhao, Dali Kaafar, and Yang Xiang, The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services, Privacy Enhancing Technologies Symposium (PETS), 2021
    • Jason Ly and Minhui Xue, Poster: Dissecting the Cryptographic Code Exchange, The Network and Distributed System Security Symposium (NDSS), 2021 
    • Jialin Wen, Benjamin Zi Hao Zhao, Minhui Xue, Alina Oprea, and Haifeng Qian, With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Linear Regression Models, IEEE Transactions on Information Forensics & Security (TIFS), 2021
    • Guo Wei Alvin Chan, Lei Ma, Felix Juefei-Xu, Yew-Soon Ong, Xiaofei Xie, Minhui Xue, and Yang Liu, Breaking Neural Reasoning Architectures with Metamorphic Relation-Based Adversarial Examples, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
    • Liuqiao Chen, Hu Wang, Benjamin Zi Hao Zhao, Minhui Xue, and Haifeng Qian, Oriole: Thwarting Privacy against Trustworthy Deep Learning Models, Australasian Conference on Information Security and Privacy (ACISP), 2021
    • Jing Xu, Minhui Xue, and Stjepan Picek, Explainability-based Backdoor Attacks Against Graph Neural Networks, ACM Workshop on Wireless Security and Machine Learning (WiseML), 2021

    2020

    • Zhushou Tang, Ke Tang, Minhui Xue, Yuan Tian, Sen Chen, Muhammad Ikram, Tielei Wang, and Haojin Zhu, iOS, Your OS, Everybody’s OS: Vetting and Analyzing Network Services of iOS Applications, USENIX Security Symposium, 2020
    • Sen Chen, Lingling Fan, Guozhu Meng, Ting Su, Minhui Xue, Yinxing Xue, Yang Liu, and Lihua Xu, An Empirical Assessment of Security Risks of Global Android Banking Apps, IEEE International Conference on Software Engineering (ICSE), 2020
    • Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao, Haojin Zhu, and Xinpeng Zhang, Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization, IEEE Transactions on Dependable and Secure Computing (TDSC), 2020
    • Wei Wang, Ruoxi Sun, Minhui Xue, Damith C. Ranasinghe, An Automated Assessment of Android Clipboards, IEEE/ACM International Conference on Automated Software Engineering (ASE), Late Breaking Results Track, 2020
    • Ruoxi Sun, Wei Wang, Minhui Xue, Gareth Tyson, and Damith C. Ranasinghe, Poster Abstract: VenueTrace: A Privacy-by-Design COVID-19 Digital Contact Tracing Solution, ACM Conference on Embedded Networked Sensor Systems (SenSys), 2020
    • Zhaohua Wang, Zhenyu Li, Minhui Xue, and Gareth Tyson, Exploring the Eastern Frontier: A First Look at Mobile App Tracking in China, The Passive and Active Measurement (PAM), 2020 
    • Ruoxi Sun, Minhui Xue, Quality Assessment of Online Automated Privacy Policy Generators: An Empirical Study, ACM Conference on Evaluation and Assessment in Software Engineering (EASE), 2020
    • Jialin Wen, Benjamin Zi Hao Zhao, Minhui Xue, and Haifeng Qian, PALOR: Poisoning Attacks against LOgistic Regression, Australasian Conference on Information Security and Privacy (ACISP), 2020

    2019

    • Matthew Joslin, Neng Li, Shuang Hao, Minhui Xue, and Haojin Zhu, Measuring and Analyzing Search Engine Poisoning of Linguistic Collisions, IEEE Symposium on Security and Privacy (Oakland), 2019
    • Minhui Xue, Xin Yuan, Heather Lee, and Keith Ross, Sensing the Chinese Diaspora: How Mobile Apps Can Provide Insights into Global Migration Flows, IEEE International Conference on Data Mining (ICDM) Workshop, 2019
    • Qingrong Chen, Chong Xiang, Minhui Xue, Bo Li, Nikita Borisov, Dali Kaafar, and Haojin Zhu, Differentially Private Data Sharing: Sharing Models versus Sharing Data, ACM Conference on Computer and Communications Security (CCS) Workshop, 2019
    • Yuantian Miao, Ben Zi Hao Zhao, Minhui Xue, Chao Chen, Lei Pan, Jun Zhang, Dali Kaafar, and Yang Xiang, The Audio Auditor: Participant-Level Membership Inference in Internet of Things Voice Services, ACM Conference on Computer and Communications Security (CCS) Workshop, 2019
    • Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Minhui Xue, Hongxu Chen, Yang Liu, Jianjun Zhao, Bo Li, Jianxiong Yin, and Simon See, DeepHunter: A Coverage-Guided Fuzz Testing Framework for Deep Neural Networks, 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), 2019
    • Chong Xiang, Xinyu Wang, Qingrong Chen, Minhui Xue, Zhaoyu Gao, Haojin Zhu, Cailian Chen, and Qiuhua Fan, No-Jump-into-Latency in China’s Internet! Toward Last-Mile Hop Count Based IP-Geolocalization, IEEE/ACM International Symposium on Quality of Service (IWQoS), 2019
    • Sen Chen, Lingling Fan, Chunyang Chen, Minhui Xue, Yang Liu, and Lihua Xu, GUI-Squatting Attack: Automated Generation of Android Phishing Apps, IEEE Transactions on Dependable and Secure Computing (TDSC), 2019

    2018

    • Haizhong Zheng, Minhui Xue, Hao Lu, Shuang Hao, Haojin Zhu, Xiaohui Liang, and Keith Ross, Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks, The Network and Distributed System Security Symposium (NDSS), 2018
    • Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang, DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems, IEEE/ACM International Conference on Automated Software Engineering (ASE), 2018 (Distinguished Paper Award)
    • Qingshun Wang, Lintao Gu, Minhui Xue, Lihua Xu, Wenyu Niu, Liang Dou, Liang He, Tao Xie, FACTS: Automated Comprehensive Testing of FinTech Systems, ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE), Industry Track, 2018
    • Sen Chen, Ting Su, Lingling Fan, Guozhu Meng, Minhui Xue, Yang Liu, and Lihua Xu. Are Mobile Banking Apps Secure? What Can be Improved?, ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE), Industry Track, 2018
    • Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang, DeepMutation: Mutation Testing of Deep Learning Systems, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2018
    • Zhushou Tang, Minhui Xue, Guozhu Meng, Chengguo Ying, Yugeng Liu, Yangyang Li, Haojin Zhu, and Yang Liu, Securing Android Applications via Edge Assistant Third-Party Library Detection, Elsevier Computers & Security, 2018 

    2017

    • Wenqi Bu, Minhui Xue, Lihua Xu, Yajin Zhou, Zhushou Tang, and Tao Xie, When Program Analysis Meets Mobile Security: An Industrial Study of Misusing Android Internet Sockets, ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE), Industry Track, 2017
    • Sen Chen, Minhui Xue, Lingling Fan, Shuang Hao, Lihua Xu, Haojin Zhu, and Bo Li, Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach, Elsevier Computers & Security, 2017
    • Qingrong Chen, Minhui Xue, Chong Xiang, Bo Li, Haizhong Zheng, and Haojin Zhu, Do We Need Original Data for Training? Toward Designing Privacy-Preserving Machine Learning, Deep Learning Security Workshop (DLSRF), 2017 (Research Forum Award)
    2016

    • Minhui Xue, Cameron L. Ballard, Kelvin Liu, Carson L. Nemelka, Yanqiu Wu, Keith W. Ross, and Haifeng Qian, You Can Yak but You Can’t Hide: Localizing Anonymous Social Network Users, ACM Conference on Internet Measurement Conference (IMC), 2016
    • Minhui Xue, Gabriel Magno, Evandro Cunha, Virgilio Almeida, and Keith W. Ross, The Right to be Forgotten in the Media: A Data-Driven Study, Proceedings on Privacy Enhancing Technologies (PETS), 2016
    • Lingling Fan, Minhui Xue, Sen Chen, Lihua Xu, and Haojin Zhu, POSTER: Accuracy vs. Time Cost: Detecting Android Malware through Pareto Ensemble Pruning, ACM Conference on Computer and Communications Security 2016 (CCS), 2016
    • Sen Chen, Minhui Xue, and Lihua Xu, Poster: Towards Adversarial Detection of Mobile Malware, ACM International Conference on Mobile Computing and Networking (MobiCom), 2016
    • Sen Chen, Minhui Xue, Zhushou Tang, Lihua Xu, and Haojin Zhu, StormDroid: A Streaminglized Machine Learning-Based System for Detecting Android Malware, ACM on Asia Conference on Computer and Communications Security (AsiaCCS), 2016
    • Minhui Xue, Yong Liu, Keith W. Ross, and Haifeng Qian, Thwarting Privacy Protection in Location-Based Social Discovery Services, Security and Communication Networks, 2016

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Entry last updated: Tuesday, 29 Mar 2022