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

Authors: William Watkins, Heehwan Wang, Sangyoon Bae, Huan-Hsin Tseng, Jiook Cha, Samuel Yen-Chi Chen, Shinjae Yoo
Published: 2024-01-15

Source: https://arxiv.org/abs/2401.07464

PDF: https://arxiv.org/pdf/2401.07464

AIにより推定されたラベル

プライバシー保護手法 量子機械学習 量子フレームワーク

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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.