While Machine Learning (ML) technologies are widely adopted in many mission critical fields to support intelligent decision-making, concerns remain about system resilience against ML-specific security attacks and privacy breaches as well as the trust that users have in these systems. In this article, we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness from a security engineering perspective, focusing on the problems in system threat analysis, design and evaluation faced in developing practical machine learning applications, in terms of robustness and user trust. Accordingly, we organize the presentation of this survey intended to facilitate the convey of the body of knowledge from this angle. We then describe a metamodel we created that represents the body of knowledge in a standard and visualized way. We further illustrate how to leverage the metamodel to guide a systematic threat analysis and security design process which extends and scales up the classic process. Finally, we propose the future research directions motivated by our findings. Our work differs itself from the existing surveys by (i) exploring the fundamental principles and best practices to support robust and trustworthy ML system development, and (ii) studying the interplay of robustness and user trust in the context of ML systems. We expect this survey provides a big picture for machine learning security practitioners.
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Bundelkhand Institute of Engineering and Technology（本德尔坎德工程技术学院）；Nanyang Technological University（南洋理工大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives
CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematics and Industrial Engineering, Polytechnique Montreal（CIRRELT与数据驱动供应链的SCALE-AI主席、数学与工业工程系，蒙特利尔大学）
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
The Chinese University of Hong Kong（香港中文大学）；University of Electronic Science and Technology of China（电子科技大学）；Peng Cheng Lab（鹏城实验室）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Verification and Validation for Trustworthy Scientific Machine Learning
Sandia National Laboratories（桑迪亚国家实验室）；The George Washington University（乔治·华盛顿大学）；University of Michigan（密歇根大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
University of Technology Sydney（悉尼科技大学）；RMIT University（皇家墨尔本理工大学）；Macquarie University（麦考瑞大学）；Arizona State University（亚利桑那州立大学）；Free University of Bozen-Bolzano（博尔扎诺自由大学）
Secure Deep Learning Engineering: A Software Quality Assurance Perspective
Harbin Institute of Technology（哈尔滨工业大学）；Nanyang Technological University（南洋理工大学）；Carnegie Mellon University（卡内基梅隆大学）；Optus Macquarie University Cyber Security Hub（Optus麦考瑞大学网络安全中心）；Kyushu University（九州大学）；University of Illinois at Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）；NVIDIA AI Tech Centre（英伟达人工智能技术中心）
A roadmap to fair and trustworthy prediction model validation in healthcare
Duke-NUS Medical School（杜克-国大医学院）；Erasmus MC University Medical Center（伊拉斯姆斯大学医学中心）；Singapore Health Services（新加坡保健服务集团）；Singapore General Hospital（新加坡中央医院）；Duke University（杜克大学）；National University of Singapore（新加坡国立大学）
A Survey of Automatic Prompt Engineering: An Optimization Perspective
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；East China Normal University（华东师范大学）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）
