Analyzing Privacy in DP-SGD Mechanisms

핵심 개념

There can be significant differences in privacy guarantees between different batch sampling methods in DP-SGD mechanisms.

초록

The content discusses the privacy guarantees of Adaptive Batch Linear Queries (ABLQ) mechanism under different batch sampling methods in Differentially Private Stochastic Gradient Descent (DP-SGD). It compares the privacy analysis of deterministic, Poisson, and shuffle batch samplers, highlighting the substantial gaps in privacy guarantees. The article provides insights into the importance of batch sampling in determining privacy guarantees and the caution needed in reporting privacy parameters for mechanisms like DP-SGD.

통계

Figure 1. Privacy parameter ε for different noise parameters σ, for fixed δ = 10^-6 and number of steps T = 100,000.
Algorithm 1 ABLQB: Adaptive Batch Linear Queries
Algorithm 2 Db,T: Deterministic Batch Sampler
Algorithm 3 Sb,T: Shuffle Batch Sampler
Algorithm 4 Pb,T: Poisson Batch Sampler

인용구

"In practice, for efficiency, the construction of batches and lots is done by randomly permuting the examples and then partitioning them into groups of the appropriate sizes." - Abadi et al. (2016)
"It is common, though inaccurate, to train without Poisson subsampling, but to report the stronger DP bounds as if amplification was used." - Ponomareva et al. (2023)

핵심 통찰 요약

How Private is DP-SGD?

by Lynn Chua,Ba... 게시일 arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17673.pdf

더 깊은 질문

How does the choice of batch sampler impact the privacy guarantees in DP-SGD mechanisms

The choice of batch sampler plays a crucial role in determining the privacy guarantees in DP-SGD mechanisms. Different batch sampling methods, such as deterministic, Poisson, and shuffle samplers, have varying impacts on the privacy analysis. For example, the shuffle batch sampler is commonly used in practice for its efficiency in constructing batches by randomly permuting examples. However, the privacy guarantees provided by shuffle sampling may not be as strong as those provided by Poisson subsampling. The choice of batch sampler can significantly affect the privacy parameters reported for DP-SGD, leading to discrepancies in the level of privacy protection offered.

What are the implications of the discrepancies in privacy guarantees between different batch sampling methods

The discrepancies in privacy guarantees between different batch sampling methods have several implications. Firstly, it highlights the importance of accurately reporting privacy parameters for DP-SGD mechanisms. Using a batch sampler that provides weaker privacy guarantees can result in underestimating the privacy loss, potentially leading to inadequate protection of sensitive data. This discrepancy raises concerns about the reliability and accuracy of privacy assurances in practical implementations of DP-SGD. Additionally, the substantial gap between privacy analyses for different batch sampling methods emphasizes the need for caution and thorough evaluation when selecting a batch sampler for DP-SGD to ensure the desired level of privacy protection.

How can the privacy analysis be improved for shuffle batch samplers in DP-SGD mechanisms

To improve the privacy analysis for shuffle batch samplers in DP-SGD mechanisms, several approaches can be considered. One way is to develop a more comprehensive understanding of the privacy implications of shuffle sampling by exploring the impact of non-differing records on the privacy guarantees. This could involve investigating the leakage of information about differing records in shuffled orders and how it affects the overall privacy protection. Additionally, refining the analysis techniques for shuffle batch samplers, such as considering specific sets that capture worst-case scenarios for privacy loss, can enhance the accuracy of privacy guarantees. By addressing these aspects, the privacy analysis for shuffle batch samplers in DP-SGD mechanisms can be improved to provide more reliable and robust privacy assurances.