Summary of Differentially Private Log-location-scale Regression Using Functional Mechanism, by Jiewen Sheng et al.
Differentially Private Log-Location-Scale Regression Using Functional Mechanism
by Jiewen Sheng, Xiaolei Fang
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Applications (stat.AP)
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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 DP-LLS regression models combine differential privacy with LLS regression using the functional mechanism. The models introduce noise into the log-likelihood function for perturbed parameter estimation, requiring sensitivity calculations to determine the noise magnitude. This ensures -differential privacy is satisfied. Simulations and case studies evaluate the performance of these models, highlighting predictor dimension, training sample size, and privacy budget as key factors affecting model performance. The results indicate a large training dataset is necessary for both decent performance and sufficient privacy protection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces new methods to keep your personal data private while still being able to learn useful patterns from it. They combine two existing techniques: LLS regression, which helps predict outcomes, and differential privacy, which protects individual information. The new models add random noise to the calculations to ensure that even if someone knew a lot about one person’s data, they wouldn’t be able to figure out much about anyone else’s. The researchers tested these models using fake data and found that having more training data helps them work better while still keeping your info private. |
Keywords
* Artificial intelligence * Log likelihood * Regression
