  Several domains increasingly rely on machine learning in their applications.
The resulting heavy dependence on data has led to the emergence of various laws
and regulations around data ethics and privacy and growing awareness of the
need for privacy-preserving machine learning (ppML). Current ppML techniques
utilize methods that are either purely based on cryptography, such as
homomorphic encryption, or that introduce noise into the input, such as
differential privacy. The main criticism given to those techniques is the fact
that they either are too slow or they trade off a model s performance for
improved confidentiality. To address this performance reduction, we aim to
leverage robust representation learning as a way of encoding our data while
optimizing the privacy-utility trade-off. Our method centers on training
autoencoders in a multi-objective manner and then concatenating the latent and
learned features from the encoding part as the encoded form of our data. Such a
deep learning-powered encoding can then safely be sent to a third party for
intensive training and hyperparameter tuning. With our proposed framework, we
can share our data and use third party tools without being under the threat of
revealing its original form. We empirically validate our results on unimodal
and multimodal settings, the latter following a vertical splitting system and
show improved performance over state-of-the-art.
