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Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis.
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ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, and Yang Zhang. 2021d · 2021
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A Inan, Melissa Chase, Esha Ghosh, and Marcello Hasegawa. 2021 · 2021
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The audio auditor: user-level membership inference in Internet of Things voice services
Yuantian Miao, Xue Minhui, Chao Chen, Lei Pan, Jun Zhang, Benjamin Zi Hao Zhao, Dali Kaafar, and Yang Xiang. 2021 · 2021
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privGAN: Protecting GANs from membership inference attacks at low cost to utility
Sumit Mukherjee, Yixi Xu, Anusua Trivedi, Nabajyoti Patowary, and Juan L Ferres. 2021 · 2021
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Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. In 2021 IEEE Symposium on Security and Privacy (S&P) . IEEE
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini. 2021 · 2021
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Membership inference attack on graph neural networks
Iyiola E Olatunji, Wolfgang Nejdl, and Megha Khosla. 2021 · 2021
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Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods. In 2021 MICCAI Workshop on Secure and Privacy-Preserving Machine Learning for Medical Imaging . Springer
William Paul, Yinzhi Cao, Miaomiao Zhang, and Phil Burlina. 2021 · 2021
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On the Difficulty of Membership Inference Attacks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . IEEE, 7892–7900
Shahbaz Rezaei and Xin Liu. 2021 · 2021
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Accuracy-Privacy Trade-off in Deep Ensemble
Shahbaz Rezaei, Zubair Shafiq, and Xin Liu. 2021 · 2021
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Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, and Lior Rokach. 2021 · 2021
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Quantifying Membership Privacy via Information Leakage
Sara Saeidian, Giulia Cervia, Tobias J Oechtering, and Mikael Skoglund. 2021 · 2021
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Reconstruction-Based Membership Inference Attacks are Easier on Difficult Problems. In 2021 IEEE/CVF International Conference on Computer Vision . IEEE
Avital Shafran, Shmuel Peleg, and Yedid Hoshen. 2021 · 2021
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Evaluating the Vulnerability of End-to-End Automatic Speech Recognition Models To Membership Inference Attacks
Muhammad A Shah, Joseph Szurley, Markus Mueller, Athanasios Mouchtaris, and Jasha Droppo. 2021 · 2021
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Membership Privacy for Machine Learning Models Through Knowledge Transfer. In Proceedings of the AAAI Conference on Artificial Intelligence . AAAI Press, 9549–9557
Virat Shejwalkar and Amir Houmansadr. 2021 · 2021
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On the Privacy Risks of Model Explanations. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society . ACM
Reza Shokri, Martin Strobel, and Yair Zick. 2021 · 2021
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Systematic evaluation of privacy risks of machine learning models. In 30th USENIX Security Symposium (USENIX Security 21) . USENIX Association, 2615–2632
Liwei Song and Prateek Mittal. 2021 · 2021
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Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, and Prateek Mittal. 2021 · 2021
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Membership Inference Attack with Multi-Grade Service Models in Edge Intelligence
Kehao Wang, Zhixin Hu, Qingsong Ai, Quan Liu, Mozi Chen, Kezhong Liu, and Yirui Cong. 2021a · 2021
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Membership Inference Attacks on Knowledge Graphs
Yu Wang and Lichao Sun. 2021 · 2021
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This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces
Ryan Webster, Julien Rabin, Loic Simon, and Frederic Jurie. 2021b · 2021
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Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan. 2021 · 2021
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On the privacy-utility trade-off in differentially private hierarchical text classification
Dominik Wunderlich, Daniel Bernau, Francesco Aldà, Javier Parra-Arnau, and Thorsten Strufe. 2021 · 2021
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A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions
Xuefei Yin, Yanming Zhu, and Jiankun Hu. 2021b · 2021
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How Does Data Augmentation Affect Privacy in Machine Learning?. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. AAAI Press, 10746–10753
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu. 2021 · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2021a · 2021
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Membership Inference Attacks Against Recommender Systems
Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhumin Chen, Pengfei Hu, and Yang Zhang. 2021b · 2021
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On the (In) Feasibility of Attribute Inference Attacks on Machine Learning Models. In 6th IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE
Benjamin Zi Hao Zhao, Aviral Agrawal, Catisha Coburn, Hassan Jameel Asghar, Raghav Bhaskar, Mohamed Ali Kaafar, Darren Webb, and Peter Dickinson. 2021 · 2021
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Resisting membership inference attacks through knowledge distillation
Junxiang Zheng, Yongzhi Cao, and Hanpin Wang. 2021 · 2021
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Practical Membership Inference Attack Against Collaborative Inference in Industrial IoT
Hanxiao Chen, Hongwei Li, Guishan Dong, Meng Hao, Guowen Xu, Xiaoming Huang, and Zhe Liu. 2022 · 2022
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Defending Privacy Against More Knowledgeable Membership Inference Attackers. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . ACM, 2026–2036
Yu Yin, Ke Chen, Lidan Shou, and Gang Chen. 2021a · 2036
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2097–2106
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. 2017 · 2097
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