May 8, 2023, 1:10 a.m. | Anshul Thakur, Tingting Zhu, Vinayak Abrol, Jacob Armstrong, Yujiang Wang, David A. Clifton

cs.CR updates on arXiv.org arxiv.org

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 …

constraints data data democratization deep learning democratization development encoding healthcare healthcare data information information leakage prevention privacy solution solutions

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