  The lack of data democratization and information leakage from trained models
hinder the development and acceptance of robust deep learning-based healthcare
solutions. This paper argues that irreversible data encoding can provide an
effective solution to achieve data democratization without violating the
privacy constraints imposed on healthcare data and clinical models. An ideal
encoding framework transforms the data into a new space where it is
imperceptible to a manual or computational inspection. However, encoded data
should preserve the semantics of the original data such that deep learning
models can be trained effectively. This paper hypothesizes the characteristics
of the desired encoding framework and then exploits random projections and
random quantum encoding to realize this framework for dense and longitudinal or
time-series data. Experimental evaluation highlights that models trained on
encoded time-series data effectively uphold the information bottleneck
principle and hence, exhibit lesser information leakage from trained models.
