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.
翻译:数据保护条例,如GDPR或加利福尼亚州《消费者隐私法》,使用户对收集到的数据有更大的控制权。删除所收集的数据往往不足以保障数据隐私,因为它常常被用来培训机器学习模式,从而暴露有关培训数据的信息。因此,还需要保证经过培训的模式不会暴露有关培训数据的信息。在本文件中,我们介绍了UnlearnSPN -- -- 一种算法,它从经过培训的总产品网络中消除单一数据点的影响,从而能够满足对需求的数据隐私要求。