论文标题
部分可观测时空混沌系统的无模型预测
Certified Data Removal in Sum-Product Networks
论文作者
论文摘要
GDPR或《加利福尼亚州消费者隐私法》等数据保护法规使用户更多地控制了有关其收集的数据。删除收集的数据通常不足以保证数据隐私，因为它通常用于训练机器学习模型，这可以暴露有关培训数据的信息。因此，另外还需要保证训练有素的模型不会暴露有关其培训数据的信息。在本文中，我们介绍了UnrearnSPN - 一种算法，该算法从训练有素的总产品网络中删除了单个数据点的影响，从而允许满足需求的数据隐私要求。
Data protection regulations like the GDPR or the California Consumer Privacy Act give users more control over the data that is collected about them. Deleting the collected data is often insufficient to guarantee data privacy since it is often used to train machine learning models, which can expose information about the training data. Thus, a guarantee that a trained model does not expose information about its training data is additionally needed. In this paper, we present UnlearnSPN -- an algorithm that removes the influence of single data points from a trained sum-product network and thereby allows fulfilling data privacy requirements on demand.
