Summary of Joint Selection: Adaptively Incorporating Public Information For Private Synthetic Data, by Miguel Fuentes et al.
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
by Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed paper develops a novel framework, called jam-pgm, for generating differentially private synthetic data by incorporating public data into graphical models. The existing methods have limitations, such as not being able to utilize public data, which can improve the quality of synthetic data. To address this issue, the authors design an adaptive measurement mechanism that jointly selects between measuring public and private data. Experimental results demonstrate that jam-pgm outperforms both publicly assisted and non-publicly assisted synthetic data generation mechanisms, even when the public data distribution is biased. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to create fake data while keeping people’s personal information safe. Currently, there are methods that use graphical models and public data, but these have limitations. The researchers developed a new method called jam-pgm that combines public and private data in a way that improves the quality of the fake data. They tested this method and found it works better than existing methods even when the public data is not accurate. |
Keywords
* Artificial intelligence * Synthetic data
