Privacy-preserving quantum machine learning using differential privacy

Makhamisa Senekane, Mhlambululi Mafu, Benedict Molibeli Taele

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


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 the same time maintaining privacy of the participants. Such cases have given birth to privacy-preserving data analyitics. Privacy is typically guaranteed by a differentially private mechanism. In this paper, we present a novel mechanism for privacy-preserving quantum machine learning. The mechanism is tested on the sensitive dataset that contains features and target labels for breast cancer prediction. The results obtained underline the utility of this mechanism.

Original languageEnglish
Title of host publication2017 IEEE AFRICON
Subtitle of host publicationScience, Technology and Innovation for Africa, AFRICON 2017
EditorsDarryn R. Cornish
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781538627754
Publication statusPublished - Nov 3 2017
EventIEEE AFRICON 2017 - Cape Town, South Africa
Duration: Sept 18 2017Sept 20 2017

Publication series

Name2017 IEEE AFRICON: Science, Technology and Innovation for Africa, AFRICON 2017


ConferenceIEEE AFRICON 2017
Country/TerritorySouth Africa
CityCape Town

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


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