Summary of On the Privacy Of Selection Mechanisms with Gaussian Noise, by Jonathan Lebensold et al.
On the Privacy of Selection Mechanisms with Gaussian Noise
by Jonathan Lebensold, Doina Precup, Borja Balle
First submitted to arxiv on: 9 Feb 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 Medium Difficulty summary: This paper revisits the analysis of two classical differentially private (DP) selection mechanisms, Report Noisy Max and Above Threshold, when instantiated using Gaussian noise. The authors show that under the assumption of bounded queries, it’s possible to provide pure ex-ante DP bounds for Report Noisy Max and pure ex-post DP bounds for Above Threshold. The resulting bounds are tight and depend on closed-form expressions that can be numerically evaluated using standard methods. Empirically, the authors find these lead to tighter privacy accounting in the high privacy, low data regime. Additionally, they propose a simple privacy filter for composing pure ex-post DP guarantees and derive a fully adaptive Gaussian Sparse Vector Technique mechanism. The authors demonstrate the effectiveness of their approach through experiments on mobility and energy consumption datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper improves our understanding of how to keep personal information private when using certain mathematical tools called differentially private selection mechanisms. These mechanisms are used in situations where we want to release information about a group, but also protect the privacy of individual members within that group. The authors show that by making some assumptions and using Gaussian noise, they can provide stronger guarantees of privacy for two specific types of selection mechanisms. This is important because it allows us to better balance the need to release information with the need to protect people’s privacy. |
