Summary of Synthetic Data Outliers: Navigating Identity Disclosure, by Carolina Trindade et al.
Synthetic Data Outliers: Navigating Identity Disclosure
by Carolina Trindade, Luís Antunes, Tânia Carvalho, Nuno Moniz
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 This paper investigates the privacy risks associated with synthetic data generation models, particularly in relation to outlier detection. It highlights that deep learning models are effective in capturing underlying characteristics but neglects re-identification risk. The authors focus on exploring outliers and demonstrate that linkage attacks can easily identify them. They also propose additional safeguards like differential privacy to prevent re-identification, albeit at the cost of data utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into how well synthetic data protects people’s personal information. It finds that even though synthetic data is good at mimicking real data, it’s still possible for someone to figure out who certain individuals are because outliers can be identified. The authors show that this re-identification risk is high and suggest ways to make synthetic data more private, but these methods might also reduce the usefulness of the data. |
Keywords
» Artificial intelligence » Deep learning » Outlier detection » Synthetic data
