Summary of Private Regression Via Data-dependent Sufficient Statistic Perturbation, by Cecilia Ferrando et al.
Private Regression via Data-Dependent Sufficient Statistic Perturbation
by Cecilia Ferrando, Daniel Sheldon
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 introduces a novel method called Data-Dependent Sufficient Statistic Perturbation (SSP) for differentially private linear regression. The authors build upon existing SSP methods, which add privacy noise to sufficient statistics in a data-independent manner. However, they argue that this approach can be improved by using data-dependent mechanisms to better approximate these statistics. They show that their new method outperforms state-of-the-art data-independent SSP and extend the result to logistic regression. The authors also explore connections between synthetic data for machine learning and differentially private models with sufficient statistics. Specifically, they demonstrate that training on synthetic data corresponds to data-dependent SSP, where the overall utility is determined by how well the mechanism answers these linear queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make sure people’s personal information remains private when using machine learning algorithms. The current method adds noise to statistics in a way that doesn’t depend on the specific data. However, the authors think this approach can be improved by considering the actual data being used. They show that their new method works better than existing ones and also apply it to another type of algorithm called logistic regression. The paper also shows how this new method is related to creating fake data for machine learning training. In some cases, using synthetic data is equivalent to using their new SSP approach, where the quality of the results depends on how well the mechanism answers certain questions about the data. |
Keywords
» Artificial intelligence » Linear regression » Logistic regression » Machine learning » Synthetic data
