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

William Watkins; Heehwan Wang; Sangyoon Bae; Huan-Hsin Tseng; Jiook Cha; Samuel Yen-Chi Chen; Shinjae Yoo

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

Quantum Physics 2024-01-17 v1 Cryptography and Security Machine Learning

Authors: William Watkins , Heehwan Wang , Sangyoon Bae , Huan-Hsin Tseng , Jiook Cha , Samuel Yen-Chi Chen , Shinjae Yoo

View on arXiv ↗ PDF ↗

Abstract

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.

Keywords

quantum machine learning privacy-preserving Private Aggregation of Teacher Ensembles quantum neural networks federated learning QPATE

Cite

@article{arxiv.2401.07464,
  title  = {Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning},
  author = {William Watkins and Heehwan Wang and Sangyoon Bae and Huan-Hsin Tseng and Jiook Cha and Samuel Yen-Chi Chen and Shinjae Yoo},
  journal= {arXiv preprint arXiv:2401.07464},
  year   = {2024}
}

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

Abstract

Keywords

Cite

Related papers