  Unlearnable examples (UEs) refer to training samples modified to be
unlearnable to Deep Neural Networks (DNNs). These examples are usually
generated by adding error-minimizing noises that can fool a DNN model into
believing that there is nothing (no error) to learn from the data. The concept
of UE has been proposed as a countermeasure against unauthorized data
exploitation on personal data. While UE has been extensively studied on images,
it is unclear how to craft effective UEs for time series data. In this work, we
introduce the first UE generation method to protect time series data from
unauthorized training by deep learning models. To this end, we propose a new
form of error-minimizing noise that can be \emph{selectively} applied to
specific segments of time series, rendering them unlearnable to DNN models
while remaining imperceptible to human observers. Through extensive experiments
on a wide range of time series datasets, we demonstrate that the proposed UE
generation method is effective in both classification and generation tasks. It
can protect time series data against unauthorized exploitation, while
preserving their utility for legitimate usage, thereby contributing to the
development of secure and trustworthy machine learning systems.
