论文解析：Machine Learning with Membership Privacy using Adversarial Regularization
这里写目录标题 摘要 论文思路总结 问题的提出 机器学习 成员推理攻击 MIN-MAX MEMBERSHIP PRIVACY GAME（最小最大化成员隐私游戏） 理论推导 实验设置 数据集 分类模型 内容推理攻击模型 实验结果 个人总结 摘要 在这篇论文中，我们专注于针对黑盒模型的tracing（menbership inference)track,即成员推理攻击。 我们引入了一种隐私机制（可以预
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相关文章
- 1. Machine Learning with Membership Privacy using Adversarial Regularization
- 2. 论文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
- 3. Learning with Privacy at Scale
- 4. Regularization method for machine learning
- 5. 论文笔记：Membership Inference Attacks Against Machine Learning Models
- 6. An Introduction to Machine Learning with Python
- 7. 【论文阅读】Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
- 8. [Machine Learning] 正则化(Regularization)
- 9. Machine Learning Andrew Ng -7. Regularization
- 10. 论文笔记（一）SecureML: A System for Scalable Privacy-Preserving Machine Learning