Summary of Quantifying and Mitigating Privacy Risks For Tabular Generative Models, by Chaoyi Zhu et al.
Quantifying and Mitigating Privacy Risks for Tabular Generative Models
by Chaoyi Zhu, Jiayi Tang, Hans Brouwer, Juan F. Pérez, Marten van Dijk, Lydia Y. Chen
First submitted to arxiv on: 12 Mar 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 intersection of generative models and privacy preservation in tabular data sharing. It examines five state-of-the-art tabular synthesizers against eight privacy attacks, highlighting the utility-privacy tradeoff. The researchers propose DP-TLDM, a differentially private tabular latent diffusion model that achieves a meaningful theoretical privacy guarantee while enhancing synthetic data quality by 35% and utility for downstream tasks by 15%. Additionally, it improves data discriminability by 50%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to make sure people can share information safely. It looks at how good or bad it is to use special computer programs called generative models to create fake data that doesn’t have personal information. The researchers tested five of these programs and found that they are not very good at keeping the fake data private. They then created a new program, DP-TLDM, that can make better fake data while still protecting people’s privacy. |
Keywords
* Artificial intelligence * Diffusion model * Synthetic data
