Privacy-preserving quantum machine learning using differential privacy

Abstract

The advance of artificial intelligence in general and machine learning in particular has resulted in the need to pay more attention to the provision of privacy to the data being anlyzed. An example of sensitive data analysis might be in the analysis of individuals' medical records. In such a case, there might be a need to draw insights from data while at… (More)

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Cite this paper

@article{Senekane2017PrivacypreservingQM, title={Privacy-preserving quantum machine learning using differential privacy}, author={Makhamisa Senekane and Mhlambululi Mafu and Benedict Molibeli Taele}, journal={2017 IEEE AFRICON}, year={2017}, pages={1432-1435} }