Data encoding for healthcare data democratization and information leakage prevention
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...
| Główni autorzy: | , , , , , |
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| Format: | Journal article |
| Język: | English |
| Wydane: |
Springer Nature
2024
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| _version_ | 1826312439280435200 |
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| author | Wang, Y Armstrong, J Thakur, A Zhu, T Abrol, V Clifton, DA |
| author_facet | Wang, Y Armstrong, J Thakur, A Zhu, T Abrol, V Clifton, DA |
| author_sort | Wang, Y |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-04-09T03:54:35Z |
| format | Journal article |
| id | oxford-uuid:4bd45b18-0527-467b-a33e-b6ab212e4498 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-09T03:54:35Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:4bd45b18-0527-467b-a33e-b6ab212e44982024-03-11T07:01:44ZData encoding for healthcare data democratization and information leakage preventionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:4bd45b18-0527-467b-a33e-b6ab212e4498EnglishSymplectic ElementsSpringer Nature2024Wang, YArmstrong, JThakur, AZhu, TAbrol, VClifton, DAThe 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. |
| spellingShingle | Wang, Y Armstrong, J Thakur, A Zhu, T Abrol, V Clifton, DA Data encoding for healthcare data democratization and information leakage prevention |
| title | Data encoding for healthcare data democratization and information leakage prevention |
| title_full | Data encoding for healthcare data democratization and information leakage prevention |
| title_fullStr | Data encoding for healthcare data democratization and information leakage prevention |
| title_full_unstemmed | Data encoding for healthcare data democratization and information leakage prevention |
| title_short | Data encoding for healthcare data democratization and information leakage prevention |
| title_sort | data encoding for healthcare data democratization and information leakage prevention |
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