The Electrocardiogram (ECG) measures the electrical cardiac activity
generated by the heart to detect abnormal heartbeat and heart attack. However,
the irregular occurrence of the abnormalities demands continuous monitoring of
heartbeats. Machine learning techniques are leveraged to automate the task to
reduce labor work needed during monitoring. In recent years, many companies
have launched products with ECG monitoring and irregular heartbeat alert. Among
all classification algorithms, the time series-based algorithm dynamic time
warping (DTW) is widely adopted to undertake the ECG classification task.
Though progress has been achieved, the DTW-based ECG classification also brings
a new attacking vector of leaking the patients' diagnosis results. This paper
shows that the ECG input samples' labels can be stolen via a side-channel
attack, Flush+Reload. In particular, we first identify the vulnerability of DTW
for ECG classification, i.e., the correlation between warping path choice and
prediction results. Then we implement an attack that leverages Flush+Reload to
monitor the warping path selection with known ECG data and then build a
predictor for constructing the relation between warping path selection and
labels of input ECG samples. Based on experiments, we find that the
Flush+Reload-based inference leakage can achieve an 84.0\% attacking success
rate to identify the labels of the two samples in DTW.

本文表明动态时间规整（DTW）算法在心电图（ECG）分类中存在漏洞，并且可以通过侧信道攻击 Flush+Reload 攻击来窃取患者的心电图标签，得到了 84% 的攻击成功率。

基于侧信道的机器学习心电图分类中的推理信息泄露

Side Channel-Assisted Inference Leakage from Machine Learning-based ECG  Classification

DTW calculates the similarity or alignment between two signals, subject to
temporal warping. However, its computational complexity grows exponentially
with the number of time-series. Although there have been algorithms developed
that are linear in the number of time-series, they are generally quadratic in
time-series length. The exception is generalized time warping (GTW), which has
linear computational cost. Yet, it can only identify simple time warping
functions. There is a need for a new fast, high-quality multisequence alignment
algorithm. We introduce trainable time warping (TTW), whose complexity is
linear in both the number and the length of time-series. TTW performs alignment
in the continuous-time domain using a sinc convolutional kernel and a
gradient-based optimization technique. We compare TTW and GTW on 85 UCR
datasets in time-series averaging and classification. TTW outperforms GTW on
67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for
the classification tasks.

本论文介绍一种新的可训练时间扭曲算法（TTW），该算法的时间复杂度线性增长，其使用 sinc 卷积核和梯度优化技术在连续时间域内执行对多个时间序列的对齐， 并在对 85 个 UCR 数据集的时间序列平均和分类任务中表现比广义时间扭曲（GTW）更优秀。