Summary of Locally Private Sampling with Public Data, by Behnoosh Zamanlooy et al.
Locally Private Sampling with Public Data
by Behnoosh Zamanlooy, Mario Diaz, Shahab Asoodeh
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 locally private sampling framework addresses the limitation of existing LDP methods by leveraging both private and public datasets from each user. It assumes users have two distributions: p for their private dataset and q for the public dataset. The goal is to design a mechanism that generates a private sample approximating p while preserving q. This objective is framed as a minimax optimization problem using f-divergence as the utility measure. The optimal mechanism is characterized for general f-divergences when p and q are discrete distributions. The framework demonstrates universality across all f-divergences, outperforming state-of-the-art locally private samplers in experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have lots of data, like pictures or words, that you want to keep private while still sharing some with others. This is a big problem because most ways to do this assume you only have one piece of data. But what if you have many pieces? The proposed method lets each user share parts of their private and public data together to create a new sample that keeps things private. It does this by finding the best way to mix these two types of data while keeping track of how different they are. This works for all kinds of data, not just one type. Tests show it’s better than other methods at doing this. |
Keywords
* Artificial intelligence * Optimization
