Summary of Personalized Differential Privacy For Ridge Regression, by Krishna Acharya et al.
Personalized Differential Privacy for Ridge Regression
by Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computers and Society (cs.CY)
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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 novel Personalized-DP Output Perturbation method (PDP-OP) enables Ridge regression models to be trained with individual per data point privacy levels, overcoming the traditional uniform privacy level constraint in differential privacy (DP). By allowing each data point to specify its own privacy requirement, PDP-OP improves the privacy-accuracy trade-offs in DP. Theoretical accuracy guarantees are provided for the resulting model, making it a crucial advancement in personalized DP machine learning. Empirical evaluations on synthetic and real datasets with diverse privacy distributions demonstrate PDP-OP’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is getting smarter! But sometimes we need to keep our data private. That’s where differential privacy (DP) comes in. Normally, we have to set one level of privacy for all the data, which can be too strict or not strict enough. This new method lets each piece of data have its own level of privacy. It’s like giving each person a special password! This helps us balance keeping our data private and getting accurate results. |
Keywords
* Artificial intelligence * Machine learning * Regression
