Secure and Robust Machine Learning for Healthcare: A Survey
<p>Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CAD...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , |
| Published: |
2020
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513561769803776 |
|---|---|
| author | Adnan Qayyum (16875936) |
| author2 | Junaid Qadir (16494902) Muhammad Bilal (737265) Ala Al-Fuqaha (4434340) |
| author2_role | author author author |
| author_facet | Adnan Qayyum (16875936) Junaid Qadir (16494902) Muhammad Bilal (737265) Ala Al-Fuqaha (4434340) |
| author_role | author |
| dc.creator.none.fl_str_mv | Adnan Qayyum (16875936) Junaid Qadir (16494902) Muhammad Bilal (737265) Ala Al-Fuqaha (4434340) |
| dc.date.none.fl_str_mv | 2020-07-31T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/rbme.2020.3013489 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Secure_and_Robust_Machine_Learning_for_Healthcare_A_Survey/24025173 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Information and computing sciences Machine learning Robustness Adversarial machine learning Medical services Machine learning Privacy |
| dc.title.none.fl_str_mv | Secure and Robust Machine Learning for Healthcare: A Survey |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.</p><h2>Other Information</h2><p>Published in: IEEE Reviews in Biomedical Engineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/rbme.2020.3013489" target="_blank">https://dx.doi.org/10.1109/rbme.2020.3013489</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a8dbd34979a87840d7464b125bc96630 |
| identifier_str_mv | 10.1109/rbme.2020.3013489 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24025173 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Secure and Robust Machine Learning for Healthcare: A SurveyAdnan Qayyum (16875936)Junaid Qadir (16494902)Muhammad Bilal (737265)Ala Al-Fuqaha (4434340)EngineeringBiomedical engineeringInformation and computing sciencesMachine learningRobustnessAdversarial machine learningMedical servicesMachine learningPrivacy<p>Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.</p><h2>Other Information</h2><p>Published in: IEEE Reviews in Biomedical Engineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/rbme.2020.3013489" target="_blank">https://dx.doi.org/10.1109/rbme.2020.3013489</a></p>2020-07-31T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/rbme.2020.3013489https://figshare.com/articles/journal_contribution/Secure_and_Robust_Machine_Learning_for_Healthcare_A_Survey/24025173CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240251732020-07-31T00:00:00Z |
| spellingShingle | Secure and Robust Machine Learning for Healthcare: A Survey Adnan Qayyum (16875936) Engineering Biomedical engineering Information and computing sciences Machine learning Robustness Adversarial machine learning Medical services Machine learning Privacy |
| status_str | publishedVersion |
| title | Secure and Robust Machine Learning for Healthcare: A Survey |
| title_full | Secure and Robust Machine Learning for Healthcare: A Survey |
| title_fullStr | Secure and Robust Machine Learning for Healthcare: A Survey |
| title_full_unstemmed | Secure and Robust Machine Learning for Healthcare: A Survey |
| title_short | Secure and Robust Machine Learning for Healthcare: A Survey |
| title_sort | Secure and Robust Machine Learning for Healthcare: A Survey |
| topic | Engineering Biomedical engineering Information and computing sciences Machine learning Robustness Adversarial machine learning Medical services Machine learning Privacy |