Certified Data Removal in Sum-Product Networks
Alexander Becker, Thomas Liebig

TL;DR
This paper introduces UnlearnSPN, an algorithm that removes the influence of individual data points from trained sum-product networks to enhance data privacy and comply with regulations like GDPR.
Contribution
UnlearnSPN is the first method to enable data removal from sum-product networks, ensuring privacy without retraining from scratch.
Findings
Effectively removes data influence from trained models
Maintains model performance after data removal
Supports compliance with privacy regulations
Abstract
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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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Data Quality and Management · Privacy, Security, and Data Protection
