論文解析：Machine Learning with Membership Privacy using Adversarial Regularization
這裏寫目錄標題 摘要 論文思路總結 問題的提出 機器學習 成員推理攻擊 MIN-MAX MEMBERSHIP PRIVACY GAME（最小最大化成員隱私遊戲） 理論推導 實驗設置 數據集 分類模型 內容推理攻擊模型 實驗結果 個人總結 摘要 在這篇論文中，我們專注於針對黑盒模型的tracing（menbership inference)track,即成員推理攻擊。 我們引入了一種隱私機制（可以預
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相關文章
- 1. Machine Learning with Membership Privacy using Adversarial Regularization
- 2. 論文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
- 3. Learning with Privacy at Scale
- 4. Regularization method for machine learning
- 5. 論文筆記：Membership Inference Attacks Against Machine Learning Models
- 6. An Introduction to Machine Learning with Python
- 7. 【論文閱讀】Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
- 8. [Machine Learning] 正則化(Regularization)
- 9. Machine Learning Andrew Ng -7. Regularization
- 10. 論文筆記（一）SecureML: A System for Scalable Privacy-Preserving Machine Learning