In: American Association for Artificial Intelligence ( AAAI), 2021 “Discovering Fully Directed Causal Networks” Osman Mian, Alexander Marx, Jilles Vreeken “Membership Leakage in Label-Only Exposures” “Membership Inference Attacks Against Recommender Systems” Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhumin Chen, Pengfei Hu, Yang Zhang “Quantifying and Mitigating Privacy Risks of Contrastive Learning” In: ACM SIGSAC Conference on Computer and Communications Security ( CCS), 2021 “When Machine Unlearning Jeopardizes Privacy” Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang In: USENIX Security Symposium ( USENIX Security), 2021 “Stealing Links from Graph Neural Networks” Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, Yang Zhang “Naming the most anomalous cluster in Hilbert Space for structures with attribute information” In: American Association for Artificial Intelligence ( AAAI), 2022 In: American Association for Artificial Intelligence ( AAAI), 2022
“Differentially Describing Groups of Graphs” Corinna Coupette, Sebastian Dalleiger, Jilles Vreeken “Property Inference Attacks Against GANs” Junhao Zhou, Yufei Chen, Chao Shen, Yang Zhang In: Annual Network and Distributed System Security Symposium ( NDSS), 2022 “Get a Model! Model Hijacking Attack Against Machine Learning Models” In: IEEE Symposium on Security and Privacy ( S&P), 2022 “Model Stealing Attacks Against Inductive Graph Neural Networks” Yun Shen, Xinlei He, Yufei Han, Yang Zhang In: IEEE European Symposium on Security and Privacy ( EuroS&P), 2022 “Dynamic Backdoor Attacks Against Machine Learning Models” Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, Yang Zhang “Inference Attacks Against Graph Neural Networks” Zhikun Zhang, Min Chen, Michael Backes, Yun Shen, Yang Zhang In: USENIX Security Symposium ( USENIX Security), 2022 “ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models” Yugeng Liu, Rui Wen, Xinlei He, Ahed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, Yang Zhang
In: Conference on Health, Inference, and Learning ( CHIL), 2022 “Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data” Shadi Rahimian, Raouf Kerkouche, Ina Kurth, Mario Fritz In: International Conference on Representation Learning ( ICLR), 2022 “RelaxLoss: Defending Membership Inference Attacks without Losing Utility”
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