Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention
This addresses privacy and accessibility issues in healthcare AI, but it is incremental as it builds on existing encoding methods like random projections and quantum encoding.
The paper tackles the problem of data democratization and information leakage in healthcare deep learning by proposing irreversible data encoding, which allows effective model training while preventing privacy violations. Experimental results show that models trained on encoded time-series data exhibit reduced information leakage.
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.