Summary of Model Inversion Attacks: a Survey Of Approaches and Countermeasures, by Zhanke Zhou et al.
Model Inversion Attacks: A Survey of Approaches and Countermeasures
by Zhanke Zhou, Jianing Zhu, Fengfei Yu, Xuan Li, Xiong Peng, Tongliang Liu, Bo Han
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The new survey aims to provide a comprehensive overview of model inversion attacks (MIAs) and their defenses in various domains, including images, texts, and graphs. The study highlights the vulnerability of neural networks and raises concerns about privacy leakage, as MIAs can extract sensitive features of private data by abusing access to well-trained models. To address this critical issue, the survey summarizes up-to-date MIA methods, highlighting their contributions, limitations, underlying modeling principles, optimization challenges, and future directions. The research community can benefit from this study’s findings, which aim to facilitate further research in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model inversion attacks (MIAs) are a new type of privacy attack that extracts sensitive features of private data for training by abusing access to well-trained models. This threat highlights the vulnerability of neural networks and raises concerns about privacy leakage. A survey is conducted to provide a comprehensive overview of MIAs and their defenses in various domains, including images, texts, and graphs. The study summarizes up-to-date MIA methods, highlighting their contributions, limitations, and future directions. |
Keywords
* Artificial intelligence * Optimization
