Machine learning models have recently enjoyed a significant increase in size
and popularity. However, this growth has created concerns about dataset
privacy. To counteract data leakage, various privacy frameworks guarantee that
the output of machine learning models does not compromise their training data.
However, this privatization comes at a cost by adding random noise to the
training process, which reduces model performance. By making models more
resistant to small changes in input and thus more stable, the necessary amount
of noise can be decreased while still protecting privacy. This paper
investigates various techniques to enhance stability, thereby minimizing the
negative effects of privatization in machine learning.

研究了提高模型稳定性的各种技术，从而最小化机器学习中隐私化的负面影响。

针对提升隐私保护的机器学习稳定性技术的新型综述

A Novel Review of Stability Techniques for Improved Privacy-Preserving  Machine Learning

The increased application of machine learning (ML) in sensitive domains
requires protecting the training data through privacy frameworks, such as
differential privacy (DP). DP requires to specify a uniform privacy level
$\varepsilon$ that expresses the maximum privacy loss that each data point in
the entire dataset is willing to tolerate. Yet, in practice, different data
points often have different privacy requirements. Having to set one uniform
privacy level is usually too restrictive, often forcing a learner to guarantee
the stringent privacy requirement, at a large cost to accuracy. To overcome
this limitation, we introduce our novel Personalized-DP Output Perturbation
method (PDP-OP) that enables to train Ridge regression models with individual
per data point privacy levels. We provide rigorous privacy proofs for our
PDP-OP as well as accuracy guarantees for the resulting model. This work is the
first to provide such theoretical accuracy guarantees when it comes to
personalized DP in machine learning, whereas previous work only provided
empirical evaluations. We empirically evaluate PDP-OP on synthetic and real
datasets and with diverse privacy distributions. We show that by enabling each
data point to specify their own privacy requirement, we can significantly
improve the privacy-accuracy trade-offs in DP. We also show that PDP-OP
outperforms the personalized privacy techniques of Jorgensen et al. (2015).

个性化差分隐私机器学习：使用个人数据点隐私级别训练 Ridge 回归模型的方法 (PDP-OP)，在提供严格隐私保证和模型准确性的前提下，改善差分隐私中的隐私 - 准确性平衡。