  A new collaborative learning, called split learning, was recently introduced,
aiming to protect user data privacy without revealing raw input data to a
server. It collaboratively runs a deep neural network model where the model is
split into two parts, one for the client and the other for the server.
Therefore, the server has no direct access to raw data processed at the client.
Until now, the split learning is believed to be a promising approach to protect
the client's raw data; for example, the client's data was protected in
healthcare image applications using 2D convolutional neural network (CNN)
models. However, it is still unclear whether the split learning can be applied
to other deep learning models, in particular, 1D CNN.
  In this paper, we examine whether split learning can be used to perform
privacy-preserving training for 1D CNN models. To answer this, we first design
and implement an 1D CNN model under split learning and validate its efficacy in
detecting heart abnormalities using medical ECG data. We observed that the 1D
CNN model under split learning can achieve the same accuracy of 98.9\% like the
original (non-split) model. However, our evaluation demonstrates that split
learning may fail to protect the raw data privacy on 1D CNN models. To address
the observed privacy leakage in split learning, we adopt two privacy leakage
mitigation techniques: 1) adding more hidden layers to the client side and 2)
applying differential privacy. Although those mitigation techniques are helpful
in reducing privacy leakage, they have a significant impact on model accuracy.
Hence, based on those results, we conclude that split learning alone would not
be sufficient to maintain the confidentiality of raw sequential data in 1D CNN
models.
