Summary of Efficient and Private Marginal Reconstruction with Local Non-negativity, by Brett Mullins et al.
Efficient and Private Marginal Reconstruction with Local Non-Negativity
by Brett Mullins, Miguel Fuentes, Yingtai Xiao, Daniel Kifer, Cameron Musco, Daniel Sheldon
First submitted to arxiv on: 1 Oct 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 authors introduce two postprocessing methods for reconstructing answers to marginal queries in differentially private algorithms: ReM (Residuals-to-Marginals) and its extension GReM-LNN (Gaussian Residuals-to-Marginals with Local Non-negativity). These methods aim to economize the privacy budget, minimize error on reconstructed answers, and enable scalability to high-dimensional datasets. The authors build upon recent work in efficient mechanisms for marginal query release, using a residual query basis that admits efficient pseudoinversion. They demonstrate the utility of these methods by applying them to improve existing private query answering mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces two new methods for improving differential privacy: ReM and GReM-LNN. These methods help make it easier to keep data private while still being able to get useful information from it. The authors use a special way of asking questions about the data, called residual queries, that makes it faster and more accurate. They test their methods on real-world problems and show that they can work well. |
