论文笔记：Membership Inference Attacks Against Machine Learning Models
Membership Inference Attacks Against Machine Learning Models 简介：这篇文章关注机器学习模型的隐私泄露问题，提出了一种成员推理攻击：给出一条样本，可以推断该样本是否在模型的训练数据集中——即便对模型的参数、结构知之甚少，该攻击仍然有效。其核心在于其提出的shadow learning技术。 问题设定 考虑多分类问题，模型的输出是一个预测向
相关文章
- 1. 论文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
- 2. 论文学习笔记 MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 3. 论文笔记：ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learn
- 4. Practical Black-Box Attacks against Machine Learning
- 5. [paper]Practical Black-Box Attacks against Machine Learning
- 6. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 7. Practical Black-Box Attacks against Machine Learning 阅读笔记
- 8. Machine Learning & Deep Learning 论文阅读笔记
- 9. 论文解析：Machine Learning with Membership Privacy using Adversarial Regularization
- 10. Classification and inference with machine learning
- 更多相关文章...
- • Docker Machine - Docker教程
- • ASP.NET Razor - 标记 - ASP.NET 教程
- • Tomcat学习笔记（史上最全tomcat学习笔记）
- • Scala 中文乱码解决
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 论文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
- 2. 论文学习笔记 MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 3. 论文笔记：ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learn
- 4. Practical Black-Box Attacks against Machine Learning
- 5. [paper]Practical Black-Box Attacks against Machine Learning
- 6. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 7. Practical Black-Box Attacks against Machine Learning 阅读笔记
- 8. Machine Learning & Deep Learning 论文阅读笔记
- 9. 论文解析：Machine Learning with Membership Privacy using Adversarial Regularization
- 10. Classification and inference with machine learning