The biomedical field is among the sectors most impacted by the increasing
regulation of Artificial Intelligence (AI) and data protection legislation,
given the sensitivity of patient information. However, the rise of synthetic
data generation methods offers a promising opportunity for data-driven
technologies. In this study, we propose a statistical approach for synthetic
data generation applicable in classification problems. We assess the utility
and privacy implications of synthetic data generated by Kernel Density
Estimator and K-Nearest Neighbors sampling (KDE-KNN) within a real-world
context, specifically focusing on its application in sepsis detection. The
detection of sepsis is a critical challenge in clinical practice due to its
rapid progression and potentially life-threatening consequences. Moreover, we
emphasize the benefits of KDE-KNN compared to current synthetic data generation
methodologies. Additionally, our study examines the effects of incorporating
synthetic data into model training procedures. This investigation provides
valuable insights into the effectiveness of synthetic data generation
techniques in mitigating regulatory constraints within the biomedical field.

在生物医学领域中，合成数据生成方法的兴起为数据驱动技术提供了有希望的机会。本研究提出了一种统计方法用于分类问题的合成数据生成。我们评估了核密度估计和 K 最近邻采样（KDE-KNN）生成的合成数据在现实环境中的实用性和隐私影响，特别关注其在败血症检测中的应用。我们强调了 KDE-KNN 相对于当前合成数据生成方法的优势，并考察了合成数据引入模型训练过程的效果。这项研究为了解合成数据生成技术在缓解生物医学领域的监管约束方面的有效性提供了宝贵的见解。

隐私保护的统计数据生成：用于脓毒症检测的应用

Privacy-Preserving Statistical Data Generation: Application to Sepsis  Detection

Data protection legislation like the European Union's General Data Protection
Regulation (GDPR) establishes the \textit{right to be forgotten}: a user
(client) can request contributions made using their data to be removed from
learned models. In this paper, we study how to remove the contributions made by
a client participating in a Federated Online Learning to Rank (FOLTR) system.
In a FOLTR system, a ranker is learned by aggregating local updates to the
global ranking model. Local updates are learned in an online manner at a
client-level using queries and implicit interactions that have occurred within
that specific client. By doing so, each client's local data is not shared with
other clients or with a centralised search service, while at the same time
clients can benefit from an effective global ranking model learned from
contributions of each client in the federation.
In this paper, we study an effective and efficient unlearning method that can
remove a client's contribution without compromising the overall ranker
effectiveness and without needing to retrain the global ranker from scratch. A
key challenge is how to measure whether the model has unlearned the
contributions from the client $c^*$ that has requested removal. For this, we
instruct $c^*$ to perform a poisoning attack (add noise to this client updates)
and then we measure whether the impact of the attack is lessened when the
unlearning process has taken place. Through experiments on four datasets, we
demonstrate the effectiveness and efficiency of the unlearning strategy under
different combinations of parameter settings.

本文研究了如何有效和高效地移除参与联邦在线学习排序系统的客户端的贡献，同时不损害整体排序模型的有效性，而无需重新训练全局排序模型。关键挑战在于如何衡量模型是否已经从请求删除的客户端 $c^*$ 处学到了贡献，我们通过在四个数据集上的实验证明了不同参数设置下取消学习策略的有效性和高效性。