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Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning

arXiv 2401.07464 ↗First posted 15 Jan 2024quant-ph cs.CR cs.LGcited by 0

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Abstract (arXiv)

The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple teacher models are trained on disjoint datasets. This study is the first to apply PATE to an ensemble of quantum neural networks (QNN) to pave a new way of ensuring privacy in quantum machine learning (QML) models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 15 Jan 2024.

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18 of 41 references.

References · 18

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2014Going Deeper with Convolutionscited by 4,231
  3. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  4. 2016Deep Learning with Differential Privacycited by 2,682
  5. 2020Extracting Training Data from Large Language Modelscited by 2,144
  6. 2020Variational Quantum Algorithmscited by 687
  7. 2020The power of quantum neural networkscited by 377
  8. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  9. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
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  11. 2020Quantum Long Short-Term Memorycited by 118
  12. 2018A General Approach to Adding Differential Privacy to Iterative Training Procedurescited by 84
6 more
  1. 2014Differential Privacy and Machine Learning: a Survey and Reviewcited by 66
  2. 2020Quantum Convolutional Neural Networks for High Energy Physics Data Analysiscited by 64
  3. 2021Quantum machine learning with differential privacycited by 30
  4. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  5. 2018The Expressive Power of Parameterized Quantum Circuitscited by 18
  6. 2016Learning Privately from Multiparty Datacited by 16

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