Heida : Software Examples for Rapid Introduction of Homomorphic Encryption for Privacy Preservation of Health Data
(2023) In Studies in Health Technology and Informatics 302. p.267-271- Abstract
Adequate privacy protection is crucial for implementing modern AI algorithms in medicine. With Fully Homomorphic Encryption (FHE), a party without access to the secret key can perform calculations and advanced analytics on encrypted data without taking part of either the input data or the results. FHE can therefore work as an enabler for situations where computations are carried out by parties that are denied plain text access to sensitive data. It is a scenario often found with digital services that process personal health-related data or medical data originating from a healthcare provider, for example, when the service is delivered by a third-party service provider located in the cloud. There are practical challenges to be aware of... (More)
Adequate privacy protection is crucial for implementing modern AI algorithms in medicine. With Fully Homomorphic Encryption (FHE), a party without access to the secret key can perform calculations and advanced analytics on encrypted data without taking part of either the input data or the results. FHE can therefore work as an enabler for situations where computations are carried out by parties that are denied plain text access to sensitive data. It is a scenario often found with digital services that process personal health-related data or medical data originating from a healthcare provider, for example, when the service is delivered by a third-party service provider located in the cloud. There are practical challenges to be aware of when working with FHE. The current work aims to improve accessibility and reduce barriers to entry by providing code examples and recommendations to aid developers working with health data in developing FHE-based applications. HEIDA is available on the GitHub repository: https://github.com/rickardbrannvall/HEIDA.
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- author
- Brännvall, Rickard ; Forsgren, Henrik and Linge, Helena LU
- publishing date
- 2023-05-18
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Privacy, Computer Security, Software, Algorithms
- in
- Studies in Health Technology and Informatics
- volume
- 302
- pages
- 267 - 271
- publisher
- IOS Press
- external identifiers
-
- scopus:85159768596
- pmid:37203660
- ISSN
- 0926-9630
- DOI
- 10.3233/SHTI230116
- language
- English
- LU publication?
- no
- id
- 2a531142-4cd0-489f-a95e-61d79aeb5e93
- date added to LUP
- 2023-11-01 08:42:41
- date last changed
- 2024-04-19 03:20:52
@article{2a531142-4cd0-489f-a95e-61d79aeb5e93,
abstract = {{<p>Adequate privacy protection is crucial for implementing modern AI algorithms in medicine. With Fully Homomorphic Encryption (FHE), a party without access to the secret key can perform calculations and advanced analytics on encrypted data without taking part of either the input data or the results. FHE can therefore work as an enabler for situations where computations are carried out by parties that are denied plain text access to sensitive data. It is a scenario often found with digital services that process personal health-related data or medical data originating from a healthcare provider, for example, when the service is delivered by a third-party service provider located in the cloud. There are practical challenges to be aware of when working with FHE. The current work aims to improve accessibility and reduce barriers to entry by providing code examples and recommendations to aid developers working with health data in developing FHE-based applications. HEIDA is available on the GitHub repository: https://github.com/rickardbrannvall/HEIDA.</p>}},
author = {{Brännvall, Rickard and Forsgren, Henrik and Linge, Helena}},
issn = {{0926-9630}},
keywords = {{Privacy; Computer Security; Software; Algorithms}},
language = {{eng}},
month = {{05}},
pages = {{267--271}},
publisher = {{IOS Press}},
series = {{Studies in Health Technology and Informatics}},
title = {{Heida : Software Examples for Rapid Introduction of Homomorphic Encryption for Privacy Preservation of Health Data}},
url = {{http://dx.doi.org/10.3233/SHTI230116}},
doi = {{10.3233/SHTI230116}},
volume = {{302}},
year = {{2023}},
}