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class:gradsec2026 [2026/05/15 10:37] hanwoo [C2. Model Poisoning & Backdoor Attacks] |
class:gradsec2026 [2026/05/15 10:38] (current) hanwoo [C3. Privacy Attacks on Machine Learning] |
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| ==== C3. Privacy Attacks on Machine Learning ==== | ==== C3. Privacy Attacks on Machine Learning ==== | ||
| - | - **Extracting Training Data from Large Language Models** | + | -<fc red>(kawk)</fc> **Extracting Training Data from Large Language Models** |
| * Nicholas Carlini et al., USENIX Security 2021 | Pages: 17 | Difficulty: 3/5 | * Nicholas Carlini et al., USENIX Security 2021 | Pages: 17 | Difficulty: 3/5 | ||
| * Abstract: Demonstrates that large language models like GPT-2 memorize and can be made to emit verbatim training data including personal information, phone numbers, and copyrighted content. The paper raises serious privacy concerns for LLMs trained on web data and shows that model size correlates with memorization capability. | * Abstract: Demonstrates that large language models like GPT-2 memorize and can be made to emit verbatim training data including personal information, phone numbers, and copyrighted content. The paper raises serious privacy concerns for LLMs trained on web data and shows that model size correlates with memorization capability. | ||
class/gradsec2026.1778816263.txt.gz · Last modified: 2026/05/15 10:37 by hanwoo · [Old revisions]
