Differential privacy mechanisms such as the Gaussian or Laplace mechanism
have been widely used in data analytics for preserving individual privacy.
However, they are mostly designed for continuous outputs and are unsuitable for
scenarios where discrete values are necessary. Although various quantization
mechanisms were proposed recently to generate discrete outputs under
differential privacy, the outcomes are either biased or have an inferior
accuracy-privacy trade-off. In this paper, we propose a family of quantization
mechanisms that is unbiased and differentially private. It has a high degree of
freedom and we show that some existing mechanisms can be considered as special
cases of ours. To find the optimal mechanism, we formulate a linear
optimization that can be solved efficiently using linear programming tools.
Experiments show that our proposed mechanism can attain a better
privacy-accuracy trade-off compared to baselines.

该研究提出一种无偏差且具有差分隐私的量化机制，通过线性优化方法可以高效地找到最佳机制，实验证明该机制与基准方法相比可以获得更好的准确性和隐私权衡。

隐私感知的线性规划随机量化

Privacy-Aware Randomized Quantization via Linear Programming

Federated learning (FL) is a framework which allows multiple users to jointly
train a global machine learning (ML) model by transmitting only model updates
under the coordination of a parameter server, while being able to keep their
datasets local. One key motivation of such distributed frameworks is to provide
privacy guarantees to the users. However, preserving the users' datasets
locally is shown to be not sufficient for privacy. Several differential privacy
(DP) mechanisms have been proposed to provide provable privacy guarantees by
introducing randomness into the framework, and majority of these mechanisms
rely on injecting additive noise. FL frameworks also face the challenge of
communication efficiency, especially as machine learning models grow in
complexity and size. Quantization is a commonly utilized method, reducing the
communication cost by transmitting compressed representation of the underlying
information. Although there have been several studies on DP and quantization in
FL, the potential contribution of the quantization method alone in providing
privacy guarantees has not been extensively analyzed yet. We in this paper
present a novel stochastic quantization method, utilizing a mixed geometric
distribution to introduce the randomness needed to provide DP, without any
additive noise. We provide convergence analysis for our framework and
empirically study its performance.

本文提出了一种新颖的随机量化方法，利用混合几何分布引入所需的随机性，以提供差分隐私，而不引入任何附加噪声，并通过收敛分析和实证研究了该方法的性能。

QMGeo：基于混合截断几何分布的随机量化差分隐私联邦学习

QMGeo: Differentially Private Federated Learning via Stochastic  Quantization with Mixed Truncated Geometric Distribution

We explore Reconstruction Robustness (ReRo), which was recently proposed as
an upper bound on the success of data reconstruction attacks against machine
learning models. Previous research has demonstrated that differential privacy
(DP) mechanisms also provide ReRo, but so far, only asymptotic Monte Carlo
estimates of a tight ReRo bound have been shown. Directly computable ReRo
bounds for general DP mechanisms are thus desirable. In this work, we establish
a connection between hypothesis testing DP and ReRo and derive closed-form,
analytic or numerical ReRo bounds for the Laplace and Gaussian mechanisms and
their subsampled variants.

我们建立了假设检验差分隐私与重建鲁棒性的联系，并推导出来自拉普拉斯和高斯机制以及其子采样变体的闭式、解析或数值重建鲁棒性上界。