論文筆記：Membership Inference Attacks Against Machine Learning Models
Membership Inference Attacks Against Machine Learning Models 簡介：這篇文章關注機器學習模型的隱私泄露問題，提出了一種成員推理攻擊：給出一條樣本，可以推斷該樣本是否在模型的訓練數據集中——即便對模型的參數、結構知之甚少，該攻擊仍然有效。其核心在於其提出的shadow learning技術。 問題設定 考慮多分類問題，模型的輸出是一個預測向
相關文章
- 1. 論文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
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相關文章
- 1. 論文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
- 2. 論文學習筆記 MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 3. 論文筆記：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 閱讀筆記
- 8. Machine Learning & Deep Learning 論文閱讀筆記
- 9. 論文解析：Machine Learning with Membership Privacy using Adversarial Regularization
- 10. Classification and inference with machine learning