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papersSEP 10 04:00 UTC

arXiv study characterizes privacy risks of quantum machine learning

A new preprint on arXiv examines how privacy leakage manifests in quantum machine learning systems. The authors argue that QML inherits privacy risks from classical machine learning while also introducing new attack surfaces tied to what they describe as quantum-native access. The work aims to lay groundwork for systematically characterizing and mitigating these risks.

arXivclassical-machine-learningprivacy-risksquantum-machine-learningquantum-native-access

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGCharacterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access ↗SEP 10 04:00 UTC

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