On Outsourcing Artificial Neural Network Learning of Privacy-Sensitive Medical Data to the Cloud Article Swipe
Dimitrios Melissourgos
,
Hanzhi Gao
,
Chaoyi Ma
,
Shigang Chen
,
Samuel S. Wu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/ictai52525.2021.00062
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/ictai52525.2021.00062
Machine learning and artificial neural networks (ANNs) have been at the forefront of medical research in the last few years. It is well known that ANNs benefit from big data and the collection of the data is often decentralized, meaning that it is stored in different computer systems. There is a practical need to bring the distributed data together with the purpose of training a more accurate ANN. However, the privacy concern prevents medical institutes from sharing patient data freely. Federated learning and multi-party computation have been proposed to address this concern. However, they require the medical data collectors to participate in the deep-learning computations of the data users, which is inconvenient or even infeasible in practice. In this paper, we propose to use matrix masking for privacy protection of patient data. It allows the data collectors to outsource privacy-sensitive medical data to the cloud in a masked form, and allows the data users to outsource deep learning to the cloud as well, where the ANN models can be trained directly from the masked data. Our experimental results on deep-learning models for diagnosis of Alzheimer's disease and Parkinson's disease show that the diagnosis accuracy of the models trained from the masked data is similar to that of the models from the original patient data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ictai52525.2021.00062
- OA Status
- green
- Cited By
- 6
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4200188535Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/ictai52525.2021.00062Digital Object Identifier
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On Outsourcing Artificial Neural Network Learning of Privacy-Sensitive Medical Data to the CloudWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-01Full publication date if available
- Authors
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Dimitrios Melissourgos, Hanzhi Gao, Chaoyi Ma, Shigang Chen, Samuel S. WuList of authors in order
- Landing page
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https://doi.org/10.1109/ictai52525.2021.00062Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/8796752Direct OA link when available
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Cloud computing, Outsourcing, Artificial neural network, Computer science, Information privacy, Artificial intelligence, Computer security, Business, Operating system, MarketingTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 2, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
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47Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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