Summary of Exactly Minimax-optimal Locally Differentially Private Sampling, by Hyun-young Park et al.
Exactly Minimax-Optimal Locally Differentially Private Sampling
by Hyun-Young Park, Shahab Asoodeh, Si-Hyeon Lee
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 paper investigates the privacy-utility trade-off (PUT) of private sampling under local differential privacy, with potential applications to generative models. The authors define the fundamental PUT of private sampling using the f-divergence between original and sampling distributions as the utility measure. They characterize the exact PUT for both finite and continuous data spaces under mild conditions on the data distributions, proposing universally optimal sampling mechanisms that are efficient in terms of theoretical utilities for finite data space and empirical utilities for continuous data space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to balance privacy and usefulness when sampling data privately. It tries to understand the trade-off between these two goals and finds ways to do better than previous methods. The authors use a specific way to measure how well their method does, called f-divergence, which is important for understanding what’s going on. |
