发表机构
University of Pittsburgh; Cisco Research（匹兹堡大学; 思科研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究量子原生访问下QML的隐私风险，通过成员推断攻击证明增强量子访问和计算能力可带来理论及实证隐私泄露，揭示现有研究低估了此类风险。
AI 中文摘要
量子机器学习（QML）通过利用量子计算处理机器学习任务，已展现出快速进展。与此同时，伴随QML的隐私风险也开始受到研究，这些风险既继承了“经典”机器学习中的隐私泄露渠道，也包含量子特有的风险。现有关于隐私保护QML的工作主要聚焦于QML即服务（QML-as-a-service）场景，该场景通常假设QML模型所有者仅向查询提供经典比特输出，而用户（及 adversaries）仅具备经典计算能力。然而，这一观点在量子原生世界中日益受到挑战，因为具备量子能力的用户/对手可能同时拥有量子计算能力，并能访问服务提供商输出的量子信息。在本文中，我们旨在弥合这一差距，通过考察针对QML模型的成员推断攻击，证明增加量子访问和量子计算能力可带来可证明的理论隐私泄露和实证对抗优势。然而，QML的概率特性在理论和实证对抗优势之间引入了差距。这些结果表明，现有关于QML模型隐私泄露的研究低估了在涌现量子原生访问机制下的隐私泄露，我们希望这能为考察量子原生世界中QML潜在隐私泄露迈出第一步。这些结果表明，现有关于QML模型隐私泄露的研究低估了在涌现量子原生访问机制下的隐私泄露，我们希望这能为考察量子原生世界中QML潜在隐私泄露迈出第一步。
英文摘要
Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accompanying QML is also starting to be studied, which inherit privacy leakage channels from "classical" ML and also quantum-unique risks. Existing work on privacy-preserving QML largely focuses on a QML-as-a-service scenario, which generally assumes that the QML model owner provides only classical bit outputs to queries, while users (and adversaries) have only classical computing abilities. However, this view is increasingly challenged in a quantum-native world of quantum-capable users/adversaries, which may have access to both quantum computing abilities and access to quantum information output from service providers. In this paper, we aim to bridge this gap by examining membership inference attacks against QML models by demonstrating that increasing quantum access and quantum computing abilities provides provable theoretical privacy leakage and empirical adversarial gain. However, the probabilistic nature of QML introduces a gap between theoretical and empirical adversarial advantage. These results show that existing research on privacy leakage in QML models underestimates privacy leakage in emergent quantum-native access regimes, and we hope to establish a first step in examining potential privacy leakages for QML in the quantum-native world.
