mlsanityData to 7 Oct 2026

Certified Data Removal in Sum-Product Networks

arXiv 2210.01451 ↗First posted 4 Oct 2022cs.LG cs.CRcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 4 Oct 2022.

Builds on

11 of 16 references.

References · 11

  1. 2019Machine Unlearningcited by 1,020
  2. 2019Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networkscited by 599
  3. 2019Certified Data Removal from Machine Learning Modelscited by 512
  4. 2019Making AI Forget You: Data Deletion in Machine Learningcited by 447
  5. 2020Amnesiac Machine Learningcited by 305
  6. 2020Mixed-Privacy Forgetting in Deep Networkscited by 158
  7. 2020Machine Unlearning for Random Forestscited by 125
  8. 2020Coded Machine Unlearningcited by 35
  9. 2019SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networkscited by 26
  10. 2013The Randomized Dependence Coefficientcited by 24
  11. 2020CryptoSPN: Privacy-preserving Sum-Product Network Inferencecited by 3

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.