暂无分享,去创建一个
[1] Somesh Jha,et al. Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing , 2014, USENIX Security Symposium.
[2] Mario Fritz,et al. ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models , 2018, NDSS.
[3] Fabio Roli,et al. Poisoning attacks to compromise face templates , 2013, 2013 International Conference on Biometrics (ICB).
[4] Binghui Wang,et al. Stealing Hyperparameters in Machine Learning , 2018, 2018 IEEE Symposium on Security and Privacy (SP).
[5] Binghui Wang,et al. Backdoor Attacks to Graph Neural Networks , 2020, ArXiv.
[6] Amir Houmansadr,et al. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[7] Reza Shokri,et al. Machine Learning with Membership Privacy using Adversarial Regularization , 2018, CCS.
[8] Somesh Jha,et al. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.
[9] Stephan Günnemann,et al. Adversarial Attacks on Graph Neural Networks , 2019, GI-Jahrestagung.
[10] David Berthelot,et al. High Accuracy and High Fidelity Extraction of Neural Networks , 2020, USENIX Security Symposium.
[11] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[12] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[13] Úlfar Erlingsson,et al. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks , 2018, USENIX Security Symposium.
[14] Yang Zhang,et al. Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning , 2019, USENIX Security Symposium.
[15] David Evans,et al. Evaluating Differentially Private Machine Learning in Practice , 2019, USENIX Security Symposium.
[16] Liming Zhu,et al. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense , 2019 .
[17] Jianping Pan,et al. Disclose More and Risk Less: Privacy Preserving Online Social Network Data Sharing , 2020, IEEE Transactions on Dependable and Secure Computing.
[18] Fan Zhang,et al. Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.
[19] Le Song,et al. Adversarial Attack on Graph Structured Data , 2018, ICML.
[20] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[21] Vitaly Shmatikov,et al. Exploiting Unintended Feature Leakage in Collaborative Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[22] Mathias Humbert,et al. A Survey on Interdependent Privacy , 2019, ACM Comput. Surv..
[23] Binghui Wang,et al. Attacking Graph-based Classification via Manipulating the Graph Structure , 2019, CCS.
[24] Percy Liang,et al. Certified Defenses for Data Poisoning Attacks , 2017, NIPS.
[25] Somesh Jha,et al. Exploring Connections Between Active Learning and Model Extraction , 2018, USENIX Security Symposium.
[26] Kai Peng,et al. SocInf: Membership Inference Attacks on Social Media Health Data With Machine Learning , 2019, IEEE Transactions on Computational Social Systems.
[27] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[28] Soumen Chakrabarti,et al. Privacy Preserving Link Prediction with Latent Geometric Network Models , 2019, ArXiv.
[29] Kilian Q. Weinberger,et al. Simplifying Graph Convolutional Networks , 2019, ICML.
[30] Michael Backes,et al. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples , 2019, CCS.
[31] Nikita Borisov,et al. Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations , 2018, CCS.
[32] Seong Joon Oh,et al. Towards Reverse-Engineering Black-Box Neural Networks , 2017, ICLR.
[33] Somesh Jha,et al. Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting , 2017, 2018 IEEE 31st Computer Security Foundations Symposium (CSF).
[34] Minghong Fang,et al. Local Model Poisoning Attacks to Byzantine-Robust Federated Learning , 2019, USENIX Security Symposium.
[35] Raghav Bhaskar,et al. On Inferring Training Data Attributes in Machine Learning Models , 2019, ArXiv.
[36] Jan Eric Lenssen,et al. Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.
[37] Michael P. Wellman,et al. Towards the Science of Security and Privacy in Machine Learning , 2016, ArXiv.
[38] Li Wang,et al. Privacy-Preserving Graph Neural Network for Node Classification , 2020, ArXiv.
[39] Stephan Günnemann,et al. Adversarial Attacks on Neural Networks for Graph Data , 2018, KDD.
[40] Ghazaleh Beigi,et al. Privacy in Social Media: Identification, Mitigation and Applications , 2018, ArXiv.