用量子机器学习检测生理数据篡改，对标签翻转攻击效果更好。
Detection of Physiological Data Tampering Attacks with Quantum Machine Learning
- 采用量子机器学习对比经典模型，识别生理数据篡改
- 标签翻转攻击下准确率75%-95%，优于传统方法
- 对隐蔽扰动攻击仍具优势，适合医疗安全研究者
基于云端的医疗设备和可穿戴传感器广泛应用，使生理数据易受篡改，威胁医疗系统可靠性，甚至危及生命。现有机器学习虽可用于异常检测，但量子机器学习（QML）在生理传感数据上的表现尚不明确。本研究评估了QML在两类白盒攻击——数据投毒与对抗扰动——中的检测能力。结果表明，QML在标签翻转攻击中表现更优，准确率达75%-95%，具体取决于数据类型与攻击严重程度。这得益于量子算法处理高维复杂数据的能力。然而，对于不改变统计特性的隐蔽对抗扰动，QML与经典模型均表现不佳，准确率约45%-65%，但在部分情况下仍优于传统算法。
原文摘要 · Abstract (English)
The widespread use of cloud-based medical devices and wearable sensors has made physiological data susceptible to tampering. These attacks can compromise the reliability of healthcare systems which can be critical and life-threatening. Detection of such data tampering is of immediate need. Machine learning has been used to detect anomalies in datasets but the performance of Quantum Machine Learning (QML) is still yet to be evaluated for physiological sensor data. Thus, our study compares the effectiveness of QML for detecting physiological data tampering, focusing on two types of white-box attacks: data poisoning and adversarial perturbation. The results show that QML models are better at identifying label-flipping attacks, achieving accuracy rates of 75%-95% depending on the data and attack severity. This superior performance is due to the ability of quantum algorithms to handle complex and high-dimensional data. However, both QML and classical models struggle to detect more sophisticated adversarial perturbation attacks, which subtly alter data without changing its statistical properties. Although QML performed poorly against this attack with around 45%-65% accuracy, it still outperformed classical algorithms in some cases.
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