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

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

ICASSP 2024accepted0 citations

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

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

Abstract

The utility of machine learning has rapidly expanded in the last two decades and presented an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple distributed teachers are trained on disjoint data sets. 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).

BibTeX

@inproceedings{icassp2024_quantumprivacyag,
  title = {Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy Preserving Quantum Machine Learning},
  author = {William M. Watkins and Heehwan Wang and Sangyoon Bae and Huan-Hsin Tseng and Jiook Cha and Samuel Yen-Chi Chen and Shinjae Yoo},
  booktitle = {ICASSP 2024},
  year = {2024}
}