Differential Privacy-enabled Federated Learning for Sensitive Health Data Article Swipe
Olivia Choudhury
,
Aris Gkoulalas-Divanis
,
Theodoros Salonidis
,
Issa Sylla
,
Yoonyoung Park
,
Grace Hsu
,
Amar K. Das
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.02578
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.02578
Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different sites. The framework offers two levels of privacy protection. First, it does not move or share raw data across sites or with a centralized server during the model training process. Second, it uses a differential privacy mechanism to further protect the model from potential privacy attacks. We perform a comprehensive evaluation of our approach on two healthcare applications, using real-world electronic health data of 1 million patients. We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1910.02578
- https://arxiv.org/pdf/1910.02578
- OA Status
- green
- Cited By
- 169
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2978648093
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2978648093Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1910.02578Digital Object Identifier
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Differential Privacy-enabled Federated Learning for Sensitive Health DataWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-10-07Full publication date if available
- Authors
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Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, Amar K. DasList of authors in order
- Landing page
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https://arxiv.org/abs/1910.02578Publisher landing page
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https://arxiv.org/pdf/1910.02578Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1910.02578Direct OA link when available
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Differential privacy, Computer science, Federated learning, Internet privacy, Differential (mechanical device), Data mining, Artificial intelligence, Engineering, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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169Total citation count in OpenAlex
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2025: 17, 2024: 27, 2023: 40, 2022: 32, 2021: 36Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.demonstrate | 138 |
| abstract_inverted_index.distributed | 15, 63 |
| abstract_inverted_index.feasibility | 140 |
| abstract_inverted_index.integrating | 34 |
| abstract_inverted_index.maintaining | 156 |
| abstract_inverted_index.protection. | 78 |
| abstract_inverted_index.differential | 104 |
| abstract_inverted_index.transferring | 32 |
| abstract_inverted_index.applications, | 127 |
| abstract_inverted_index.comprehensive | 119 |
| abstract_inverted_index.effectiveness | 142 |
| abstract_inverted_index.person-specific | 26 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |