Summary of Gone but Not Forgotten: Improved Benchmarks For Machine Unlearning, by Keltin Grimes et al.
Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning
by Keltin Grimes, Collin Abidi, Cole Frank, Shannon Gallagher
First submitted to arxiv on: 29 May 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 In this paper, researchers address privacy concerns in machine learning models by proposing new evaluation methods for “machine unlearning” algorithms. These algorithms aim to efficiently update trained models to comply with data deletion requests while maintaining performance. The authors highlight three shortcomings in current evaluations and present alternative methods to better assess the effectiveness of these algorithms. Experiments on state-of-the-art algorithms on computer vision datasets demonstrate the utility of the new evaluation approaches, providing a more comprehensive understanding of the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can be vulnerable to attacks that reveal information about their training data. To address this issue, “machine unlearning” algorithms aim to update trained models to comply with data deletion requests while maintaining performance. Several existing algorithms show some level of privacy gain, but they are often evaluated using simple methods that don’t represent realistic threats. This paper proposes new evaluation methods for machine unlearning algorithms and shows how these approaches can be used to better understand the effectiveness of these algorithms. |
Keywords
» Artificial intelligence » Machine learning
