Certified Data Removal in Sum-Product Networks

被引:0
|
作者
Becker, Alexander [1 ]
Liebig, Thomas [1 ]
机构
[1] TU Dortmund Univ, Chair Artificial Intelligence, Comp Sci, Dortmund, Germany
关键词
Sum-Product Networks; Data Privacy; Unlearning; Forgetting; Trustworthy ML;
D O I
10.1109/ICKG55886.2022.00010
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页码:14 / 21
页数:8
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