Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom, Samuel, Giacomelli, Irene, Fredrikson, Matt, Jha, Somesh·2018·2018 IEEE 31st Computer Security Foundations Symposium (CSF)

Cited by (998)

Items 101–200 of 998

  1. Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali
    Víctor Gómez‐Escalonilla, Pedro Martínez‐Santos, Miguel Martín-Loeches · 2022 · Hydrology and earth system sciences
  2. A review of mechanistic learning in mathematical oncology
    John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro Köhn‐Luque, Sarah C. Brüningk · 2024 · Frontiers in Immunology
  3. Survey: Leakage and Privacy at Inference Time
    Jegorova, Marija, Kaul, Chaitanya, Mayor, Charlie, O'Neil, Alison Q., Weir, Alexander, Murray-Smith, Roderick, Tsaftaris, Sotirios A. · 2022 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  4. EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning
    Hongbin Liu, Jinyuan Jia, Wenjie Qu, Neil Zhenqiang Gong · 2021 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  5. Truth Serum
    Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini · 2022 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  6. Evaluation Gaps in Machine Learning Practice
    Ben Hutchinson, Negar Rostamzadeh, Christina Greer, Katherine Heller, Vinodkumar Prabhakaran · 2022 · ACM Conference on Fairness, Accountability, and Transparency (FAccT)
  7. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
    Jia, Jinyuan, Ahmed Salem, Michael Backes A, Y Victoria Zhang, Neil Zhenqiang Gong · 2019 · arXiv (Cornell University)
  8. Membership Inference Attacks and Defenses in Classification Models
    Jiacheng Li, Ninghui Li, Bruno Ribeiro · 2021 · ACM Conference on Data and Application Security and Privacy (CODASPY)
  9. Improving Robustness to Model Inversion Attacks via Mutual Information Regularization
    Tianhao Wang, Yuheng Zhang, Ruoxi Jia · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
  10. Privacy-preserving Machine Learning through Data Obfuscation
    Tianwei Zhang, Zecheng He, Ruby Bei-Loh Lee · 2018 · arXiv (Cornell University)
  11. The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape
    Zhao, Joshua, Bagchi, Saurabh, Avestimehr, Salman, Chan, Kevin, Chaterji, Somali, Dimitriadis, Dimitris, Li, Jiacheng, Li, Ninghui, Nourian, Arash, Roth, Holger · 2025 · ACM Computing Surveys
  12. PUMA: Performance Unchanged Model Augmentation for Training Data Removal
    Wu, Ga, Hashemi, Masoud, Srinivasa, Christopher · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
  13. Towards Measuring Membership Privacy
    Yunhui Long, Vincent Bindschaedler, Carl A. Gunter · 2017 · arXiv (Cornell University)
  14. Model Inversion Attacks Against Graph Neural Networks
    Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chee‐Kong Lee, Enhong Chen · 2022 · IEEE Transactions on Knowledge and Data Engineering
  15. Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
    Lea Demelius, Roman Kern, Andreas Trügler · 2025 · ACM Computing Surveys
  16. CS-MIA: Membership inference attack based on prediction confidence series in federated learning
    Yuhao Gu, Yuebin Bai, Shubin Xu · 2022 · Journal of Information Security and Applications
  17. Differential Privacy in Deep Learning: An Overview
    Trung Ha, Tran Khanh Dang, Tran Tri Dang, Tuan Anh Truong, Manh Tuan Nguyen · 2019 · International Conference on Advanced Computing and Applications (ACOMP)
  18. DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass
    Minxin Du, Xiang Yue, Sherman S. M. Chow, Tianhao Wang, Chenyu Huang, Huan Sun · 2023 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  19. Defending Model Inversion and Membership Inference Attacks via Prediction Purification
    Ziqi Yang, Shao, Bin, Bohan Xuan, Ee‐Chien Chang, Zhang, Fan · 2020 · arXiv (Cornell University)
  20. Digestive neural networks: A novel defense strategy against inference attacks in federated learning
    Hong-Kyu Lee, Jeehyeong Kim, Se-young Ahn, Rasheed Hussain, Sunghyun Cho, Junggab Son · 2021 · Computers & Security
  21. StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing
    Anbu Huang, Yang Liu, Tianjian Chen, Yongkai Zhou, Quan Sun, Hongfeng Chai, Qiang Yang · 2021 · ACM Transactions on Intelligent Systems and Technology
  22. A Review of the Key Technology in a Blockchain Building Decentralized Trust Platform
    Weichu Deng, Teng Huang, Haiyang Wang · 2022 · Mathematics
  23. Label-Only Model Inversion Attacks: Attack With the Least Information
    Tianqing Zhu, Dayong Ye, Shuai Zhou, Bo Liu, Wanlei Zhou · 2022 · IEEE Transactions on Information Forensics and Security
  24. Node-Level Membership Inference Attacks Against Graph Neural Networks
    Xinlei He, Rui Wen, Yixin Wu, Michael Backes A, Yun Cheng Shen, Yang Zhang · 2021 · arXiv (Cornell University)
  25. Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
    Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko et al. · 2024 · Harvard Data Science Review
  26. Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability
    Stacey Truex, Ling Liu, Mehmet Emre Gürsoy, Wenqi Wei, Lei Yu · 2019 · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
  27. Privacy-preserving Voice Analysis via Disentangled Representations
    Ranya Aloufi, Hamed Haddadi, David Boyle · 2020 · Cloud Computing Security Workshop
  28. Membership Inference Attacks Against Machine Learning Models via Prediction Sensitivity
    Lan Liu, Yi Wang, Gaoyang Liu, Kai Peng, Chen Wang · 2022 · IEEE Transactions on Dependable and Secure Computing
  29. Are Attribute Inference Attacks Just Imputation?
    Bargav Jayaraman, David Evans · 2022 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  30. Industry-Scale Orchestrated Federated Learning for Drug Discovery
    Martijn Oldenhof, Gergely Ács, Balázs Pejó, Ansgar Schuffenhauer, Nicholas Holway, Noé Sturm, Arne Dieckmann, Oliver Fortmeier, Eric Boniface, Clément Mayer et al. · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
  31. SoK: Model Inversion Attack Landscape: Taxonomy, Challenges, and Future Roadmap
    Sayanton Vhaduri Dibbo · 2023 · IEEE Computer Security Foundations Workshop (CSFW)
  32. PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
    Mo, Fan, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, Nicolas Kourtellis · 2021 · arXiv (Cornell University)
  33. Athena: Probabilistic Verification of Machine Unlearning
    Sommer, David M., Song, Liwei, Wagh, Sameer, Mittal, Prateek · 2022 · Proceedings on Privacy Enhancing Technologies
  34. DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
    Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanić, Ce Zhang, Bo Li · 2021 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  35. Visual privacy attacks and defenses in deep learning: a survey
    Guangsheng Zhang, Bo Liu, Tianqing Zhu, Andi Zhou, Wanlei Zhou · 2022 · Artificial Intelligence Review
  36. An Overview of Privacy in Machine Learning
    Emiliano De Cristofaro · 2020 · arXiv (Cornell University)
  37. MIRROR: Model Inversion for Deep Learning Network with High Fidelity
    Shengwei An, Guanhong Tao, Qiuling Xu, Yingqi Liu, Guangyu Shen, Yuan Yao, Jingwei Xu, Xiangyu Zhang · 2022 · Network and Distributed System Security Symposium
  38. When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges
    Qun Wang, Haijian Sun, Rose Qingyang Hu, Arupjyoti Bhuyan · 2022 · IEEE Open Journal of the Communications Society
  39. Defenses to Membership Inference Attacks: A Survey
    Li Fang Hu, Anli Yan, Hongyang Yan, Jin Li, Teng Huang, Yingying Zhang, Changyu Dong, Chunsheng Yang · 2023 · ACM Computing Surveys
  40. P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
    Wu, Bingzhe, Zhao, Shiwan, Sun, Guangyu, Zhang, Xiaolu, Su, Zhong, Zeng, Caihong, Liu, Zhihong · 2019 · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  41. How Does Data Augmentation Affect Privacy in Machine Learning?
    Dahua Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie‐Yan Liu · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
  42. Radioactive data: tracing through training
    Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jeǵou · 2020 · arXiv (Cornell University)
  43. On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models
    Benjamin Zi Hao Zhao, Aviral Agrawal, Catisha Coburn, Hassan Jameel Asghar, Raghav Bhaskar, Mohamed Ali Kâafar, Darren Webb, Peter Dickinson · 2021 · IEEE European Symposium on Security and Privacy
  44. Security and Privacy Issues in Deep Learning: A Brief Review
    Trung Ha, Tran Khanh Dang, Trung Hieu Le, Tuan Anh Truong · 2020 · SN Computer Science
  45. Adaptive Privacy Preserving Deep Learning Algorithms for Medical Data
    Xinyue Zhang, Jiahao Ding, Maoqiang Wu, Stephen T. C. Wong, Hien Van Nguyen, Miao Pan · 2021 · IEEE Winter Conference on Applications of Computer Vision
  46. Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
    Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta · 2020 · arXiv (Cornell University)
  47. Membership Inference Attacks against Diffusion Models
    Tomoya Matsumoto, Takayuki Miura, Naoto Yanai · 2023 · IEEE Security and Privacy Workshops (SPW)
  48. Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning
    Hongsheng Hu, Xuyun Zhang, Zoran A. Salcic, Lichao Sun, Kim‐Kwang Raymond Choo, Gillian Dobbie · 2023 · IEEE Transactions on Dependable and Secure Computing
  49. Beyond Model-Level Membership Privacy Leakage: an Adversarial Approach in Federated Learning
    Jiale Chen, Jiale Zhang, Yanchao Zhao, Hao Han, Kun Zhu, Bing Chen · 2020 · International Conference on Computer Communications and Networks (ICCCN)
  50. Differential privacy in deep learning: Privacy and beyond
    Yanling Wang, Qian Wang, Lingchen Zhao, Cong Wang · 2023 · Future Generation Computer Systems
  51. DP 2 -NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring
    Shuang Dai, Fanlin Meng, Qian Wang, Xizhong Chen · 2023 · Renewable and Sustainable Energy Reviews
  52. A survey on privacy inference attacks and defenses in cloud-based Deep Neural Network
    Xiaoyu Zhang, Chao Chen, Yi Xie, Xiaofeng Chen, Jun Zhang, Yang Xiang · 2022 · Computer Standards & Interfaces
  53. Label-Only Membership Inference Attacks and Defenses in Semantic Segmentation Models
    Guangsheng Zhang, Bo Liu, Tianqing Zhu, Ming Ding, Wanlei Zhou · 2022 · IEEE Transactions on Dependable and Secure Computing
  54. Defending Against Membership Inference Attacks With High Utility by GAN
    Li Fang Hu, Jin Li, Guanbiao Lin, Shiyu Peng, Zhenxin Zhang, Yingying Zhang, Changyu Dong · 2022 · IEEE Transactions on Dependable and Secure Computing
  55. Collaborative Machine Learning: Schemes, Robustness, and Privacy
    Junbo Wang, Amitangshu Pal, Qinglin Yang, Krishna Kant, Kaiming Zhu, Song Guo · 2022 · IEEE Transactions on Neural Networks and Learning Systems
  56. Unraveling Attacks to Machine-Learning-Based IoT Systems: A Survey and the Open Libraries Behind Them
    Chao Liu, Boxi Chen, Wei Shao, Wenjun Zhang, Kelvin K. L. Wong, Yi Zhang · 2024 · IEEE Internet of Things Journal
  57. Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning
    Samuel Yeom, Irene Giacomelli, Alan Menaged, Matt Fredrikson, Somesh Jha · 2019 · Journal of Computer Security
  58. DP-Sniper: Black-Box Discovery of Differential Privacy Violations using Classifiers
    Benjamin Bichsel, Samuel Steffen, Ilija Bogunovic, Martin Vechev · 2021 · IEEE Symposium on Security and Privacy (S&P)
  59. Dataset Inference: Ownership Resolution in Machine Learning
    Pratyush Maini, Mohammad Yaghini, Nicolas Papernot · 2021 · arXiv (Cornell University)
  60. Your Model Trains on My Data? Protecting Intellectual Property of Training Data via Membership Fingerprint Authentication
    Gaoyang Liu, Tianlong Xu, Xiaoqiang Ma, Chen Wang · 2022 · IEEE Transactions on Information Forensics and Security
  61. TEAR: Exploring Temporal Evolution of Adversarial Robustness for Membership Inference Attacks Against Federated Learning
    Gaoyang Liu, Zehao Tian, Jian Chen, Chen Wang, Jiangchuan Liu · 2023 · IEEE Transactions on Information Forensics and Security
  62. PPFed: A Privacy-Preserving and Personalized Federated Learning Framework
    Guangsheng Zhang, Bo Liu, Tianqing Zhu, Ming Ding, Wanlei Zhou · 2024 · IEEE Internet of Things Journal
  63. privGAN: Protecting GANs from membership inference attacks at low cost to utility
    Sumit Mukherjee, Yixi Xu, Anusua Trivedi, Nabajyoti Patowary, Juan Lavista Ferres · 2021 · Proceedings on Privacy Enhancing Technologies
  64. Please Tell Me More: Privacy Impact of Explainability through the Lens of Membership Inference Attack
    Han Liu, Yuhao Wu, Zhiyuan Yu, Ning Zhang · 2024 · IEEE Symposium on Security and Privacy (S&P)
  65. Membership Inference via Backdooring
    Hongsheng Hu, Zoran A. Salcic, Gillian Dobbie, Jinjun Chen, Lichao Sun, Xuyun Zhang · 2022 · International Joint Conference on Artificial Intelligence
  66. Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
    Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, Prateek Mittal · 2021 · arXiv (Cornell University)
  67. A survey on membership inference attacks and defenses in machine learning
    Jun Niu, Peng Liu, Xiaoyan Zhu, Kuo Shen, Yuecong Wang, Haotian Chi, Yulong Shen, Xiaohong Jiang, Jianfeng Ma, Yuqing Zhang · 2024 · Journal of Information and Intelligence
  68. Label-Only Membership Inference Attack against Node-Level Graph Neural Networks
    Mauro Conti, Jiaxin Li, Stjepan Picek, Jing Xu · 2022 · ACM Workshop on Artificial Intelligence and Security (AISec)
  69. Survey on Privacy-Preserving Techniques for Microdata Publication
    Tânia Carvalho, Nuno Moniz, Pedro Faria, Luís Filipe Antunes · 2023 · ACM Computing Surveys
  70. Differential privacy model for blockchain based smart home architecture
    Amjad Qashlan, Priyadarsi Nanda, Manoranjan Mohanty · 2023 · Future Generation Computer Systems
  71. Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
    Oren Melamud, Chaitanya Shivade · 2019 · Clinical Natural Language Processing Workshop (ClinicalNLP)
  72. Membership Inference Attack and Defense for Wireless Signal Classifiers With Deep Learning
    Yi Shi, Yalin E. Sagduyu · 2022 · IEEE Transactions on Mobile Computing
  73. Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
    Yao Wan, Zhangqian Bi, Yang He, Jianguo Zhang, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin, Philip S. Yu · 2024 · ACM Computing Surveys
  74. PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks
    Junjie Chen, Hui Wang, Hongchang Gao, Xinghua Mindy Shi · 2021 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  75. Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
    Galtier, Mathieu N, Camille Marini · 2019 · arXiv (Cornell University)
  76. Quantifying and Mitigating Privacy Risks of Contrastive Learning
    Xinlei He, Yang Zhang · 2021 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  77. Enhance membership inference attacks in federated learning
    Xinlong He, Yang Xu, Sicong Zhang, Weida Xu, Jiale Yan · 2023 · Computers & Security
  78. A data-driven binary-classification framework for oil fingerprinting analysis
    Yifu Chen, Bing Chen, Xing Song, Qiao Kang, Xudong Ye, Baiyu Zhang · 2021 · Environmental Research
  79. Formalizing and Estimating Distribution Inference Risks
    Anshuman Suri, David Evans · 2022 · Proceedings on Privacy Enhancing Technologies
  80. A federated learning architecture for secure and private neuroimaging analysis
    Stripelis, Dimitris, Gupta, Umang, Saleem, Hamza, Dhinagar, Nikhil, Ghai, Tanmay, Anastasiou, Chrysovalantis, Sánchez, Rafael, Steeg, Greg Ver, Ravi, Srivatsan, Naveed, Muhammad et al. · 2024 · Patterns
  81. When the machine learns from users, is it helping or snooping?
    Sangwook Lee, Won-Ki Moon, Jae-Gil Lee, S. Shyam Sundar · 2022 · Computers in Human Behavior
  82. Privacy-preserving explainable AI: a survey
    Thành Tâm Nguyên, Thanh Trung Huynh, Zhao Ren, Thanh Toan Nguyen, Phi Le Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen · 2024 · Science China Information Sciences
  83. Security of federated learning for cloud‐edge intelligence collaborative computing
    Jie Yang, Jun Zheng, Zheng Zhang, Qian Chen, Duncan S. Wong, Yuanzhang Li · 2022 · International Journal of Intelligent Systems
  84. GAMIN: An Adversarial Approach to Black-Box Model Inversion
    Ulrich Aïvodji, Sébastien Gambs, Timon Ther · 2019 · arXiv (Cornell University)
  85. On the Importance of Difficulty Calibration in Membership Inference Attacks
    Lauren Watson, Chuan Guo, Graham Cormode, Alexandre Sablayrolles · 2021 · arXiv (Cornell University)
  86. Digital forgetting in large language models: a survey of unlearning methods
    Blanco-Justicia, Alberto, Jebreel, Najeeb, Manzanares-Salor, Benet, Sánchez, David, Domingo-Ferrer, Josep, Collell, Guillem, Eeik Tan, Kuan · 2025 · Artificial Intelligence Review
  87. Practical Attribute Reconstruction Attack Against Federated Learning
    Chen, Chen, Lyu, Lingjuan, Yu, Han, Chen, Gang · 2024 · IEEE Transactions on Big Data
  88. E2EGI: End-to-End Gradient Inversion in Federated Learning
    Zhaohua Li, Le Wang, Guangyao Chen, Zhiqiang Zhang, Muhammad Shafiq, Zhaoquan Gu · 2022 · IEEE Journal of Biomedical and Health Informatics
  89. Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry
    Komal Naz, Isma Farah Siddiqui, Jahwan Koo, Mohammad Ali Khan, Nawab Muhammad Faseeh Qureshi · 2022 · Applied Sciences
  90. Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study
    Mohammad Reza Afrash, Esmat Mirbagheri, Mehrnaz Mashoufi, Hadi Kazemi-Arpanahi · 2023 · BMC Medical Informatics and Decision Making
  91. Synthetic data: revisiting the privacy-utility trade-off
    Fatima Jahan Sarmin, Atiquer Rahman Sarkar, Yang Wang, Noman Mohammed · 2025 · International Journal of Information Security
  92. Not one but many Tradeoffs
    Benjamin Zi Hao Zhao, Mohamed Ali Kâafar, Nicolas Kourtellis · 2020 · Cloud Computing Security Workshop
  93. Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study
    Myeongseob Ko, Ming Jin, Chenguang Wang, Ruoxi Jia · 2023 · IEEE/CVF International Conference on Computer Vision (ICCV)
  94. Hyperparameter Optimization Strategies for Tree-Based Machine Learning Models Prediction: A Comparative Study of AdaBoost, Decision Trees, and Random Forest
    Mohsen Mohammadagha · 2025 · SSRN Electronic Journal
  95. Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-Coded Credentials
    Yizhan Huang, Yichen Li, Weibin Wu, Jianping Zhang, Michael Rung-Tsong Lyu · 2024 · Proceedings of the ACM on software engineering.
  96. On the fidelity versus privacy and utility trade-off of synthetic patient data
    Tim Adams, Colin J. Birkenbihl, Karen Otte, Hwei Geok Ng, Jonas Adrian Rieling, Anatol‐Fiete Näher, Ulrich Sax, Fabian Praßer, Holger Fröhlich, Alzheimer’s Disease Neuroimaging Initiative · 2025 · iScience
  97. Debiasing Learning for Membership Inference Attacks Against Recommender Systems
    Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren · 2022 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  98. Auditing Membership Leakages of Multi-Exit Networks
    Zheng Li, Yiyong Liu, Xinlei He, Ning Yu, Michael Backes A, Yang Zhang · 2022 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  99. A GAN-Based Defense Framework Against Model Inversion Attacks
    Xueluan Gong, Ziyao Wang, Shuaike Li, Yanjiao Chen, Qian Wang · 2023 · IEEE Transactions on Information Forensics and Security
  100. On the (In)security of Peer-to-Peer Decentralized Machine Learning
    Dario Pasquini, Mathilde Raynal, Carmela Troncoso · 2023 · IEEE Symposium on Security and Privacy (S&P)