Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation

Research article (2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2023) · cited 13× · AI/ML
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Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation

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Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation. Retrieved May 24, 2026, from https://4ort.xyz/entity/learning-in-the-dark-privacy-preserving-machine-learning-using-function-approximation
MLA “Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/learning-in-the-dark-privacy-preserving-machine-learning-using-function-approximation.
BibTeX @misc{4ortxyz_learning-in-the-dark-privacy-preserving-machine-learning-using-function-approximation_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation}}, year = {2026}, url = {https://4ort.xyz/entity/learning-in-the-dark-privacy-preserving-machine-learning-using-function-approximation}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation — https://4ort.xyz/entity/learning-in-the-dark-privacy-preserving-machine-learning-using-function-approximation (retrieved 2026-05-24)

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