‘Right to Be Forgotten’: Analyzing the Impact of Forgetting Data Using K-NN Algorithm in Data Stream Learning-Reference-Cited by-同舟云学术

‘Right to Be Forgotten’: Analyzing the Impact of Forgetting Data Using K-NN Algorithm in Data Stream Learning

Published:2022 Issue: Volume: Page:530-542
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Libera Caio^ORCID,Miranda Leandro^ORCID,Bernardini Flávia^ORCID,Mastelini Saulo^ORCID,Viterbo José^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-15086-9_34

Reference24 articles.

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4. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80. ACM (2000)

5. European Commission: ethics guidelines for trustworthy AI (2019). https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai. Accessed 17 July 2020

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1. FGT-SAMK-NN: impact of the right to be forgotten using a lazy algorithm in data stream learning;Proceedings of the 16th International Conference on Theory and Practice of Electronic Governance;2023-09-26

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