Graph has become increasingly integral to the advancement of recommendation systems, particularly with the fast development of graph neural network(GNN). By exploring the virtue of rich node features and link information, GNN is designed to provide personalized and accurate suggestions. Meanwhile, the privacy leakage of GNN in such contexts has also captured special attention. Prior work has revealed that a malicious user can utilize auxiliary knowledge to extract sensitive link data of the target graph, integral to recommendation systems, via the decision made by the target GNN model. This poses a significant risk to the integrity and confidentiality of data used in recommendation system. Though important, previous works on GNN's privacy leakage are still challenged in three aspects, i.e., limited stealing attack scenarios, sub-optimal attack performance, and adaptation against defense. To address these issues, we propose a diffusion model based link stealing attack, named DM4Steal. It differs previous work from three critical aspects. (i) Generality: aiming at six attack scenarios with limited auxiliary knowledge, we propose a novel training strategy for diffusion models so that DM4Steal is transferable to diverse attack scenarios. (ii) Effectiveness: benefiting from the retention of semantic structure in the diffusion model during the training process, DM4Steal is capable to learn the precise topology of the target graph through the GNN decision process. (iii) Adaptation: when GNN is defensive (e.g., DP, Dropout), DM4Steal relies on the stability that comes from sampling the score model multiple times to keep performance degradation to a minimum, thus DM4Steal implements successful adaptive attack on defensive GNN.
Link Stealing Attacks Against Inductive Graph Neural Networks
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全研究中心）；Hong Kong University of Science and Technology（香港科技大学）；University of Birmingham（伯明翰大学）；University of Lausanne（洛桑大学）；Duke University（杜克大学）
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
IDEAS NCBR；Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；Warsaw University of Technology（华沙理工大学）；CISPA（CISPA亥姆霍兹信息安全中心）；University of Warsaw（华沙大学）
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Saarland Informatics Campus, Germany（德国萨尔州信息学园区）
I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks
Christian Doppler Laboratory for Assurance；Transparency in Software Protection, University of Vienna Vienna Austria；SBA Research \& TU Wien Vienna Austria；IBM Research Zurich Zurich Switzerland；Institute of Information Systems Engineering, TU Wien Vienna Austria；Transparency in Software Protection, University of Vienna；SBA Research \& TU Wien；IBM Research Zurich；Institute of Information Systems Engineering, TU Wien
BESA: Boosting Encoder Stealing Attack with Perturbation Recovery
School of Cyberspace Science and Technology, Beijing Institute of Technology（网络安全科学与技术学院，北京理工大学）；Beijing Institute of Technology（北京理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Model-Guardian: Protecting against Data-Free Model Stealing Using Gradient Representations and Deceptive Predictions
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；State Key Laboratory of Cyberspace Security Defense（网络空间安全防御国家重点实验室）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Graduate School of Computer Science, Saarland University（萨尔大学计算机科学研究生院）；Bosch Center for Artificial Intelligence, Robert Bosch GmbH（罗伯特·博世有限公司博世人工智能中心）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
Beihang University（北京航空航天大学）；Institute of Dataspace（数据空间研究院）；Chinese Academy of Sciences（中国科学院）；Sensetime Group Limited（商汤集团有限公司）；Zhongguancun Laboratory（中关村实验室）
Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment
Bosch Center for Artificial Intelligence（博世人工智能中心）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Saarland University（萨尔大学）；University of Strasbourg（斯特拉斯堡大学）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）
A Model Stealing Attack Against Multi-Exit Networks
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Boston University（波士顿大学）
