  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.
