Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey
Open accessAbstract
Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.
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By integrating Differential Privacy into healthcare AI systems, researchers and clinicians can gain valuable insights from large datasets without risking the exposure of personal health information, thus maintaining patients' trust and complying with stringent privacy regulations such as GDPR and HIPAA.
“By integrating Differential Privacy into healthcare AI systems, researchers and clinicians can gain valuable insights from large datasets without risking the exposure of personal health information, thus maintaining patients' trust and complying with stringent privacy regulations such as GDPR and HIPAA [21].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [21]
These technologies enhance the security and privacy of healthcare delivery and ensure that ethical standards are upheld.
“These technologies enhance the security and privacy of healthcare delivery and ensure that ethical standards are upheld.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [21]
Differential Privacy ensures that the privacy of individual patients is not compromised when their data are used for research or to train AI models.
“Differential Privacy ensures that the privacy of individual patients is not compromised when their data are used for research or to train AI models.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [21]
Blockchain can facilitate patient engagement by giving individuals control over their health data and who can access it.
“Blockchain can facilitate patient engagement by giving individuals control over their health data and who can access it.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [21]
AIIoT adoption in healthcare is rapid and significantly enhances the quality and effectiveness of healthcare services.
“The research by [21] highlights the rapid adoption of AIIoT in healthcare, significantly enhancing the quality and effectiveness of healthcare services.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [21]
IoHT encompasses a vast network of connected healthcare devices that collect and transmit data, raising unique privacy and security concerns.
“The IoHT encompasses a vast network of connected healthcare devices that collect and transmit data, raising unique privacy and security concerns.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [21]
Researchers are exploring a variety of PETs including anonymization, cryptographic methods, and blockchain to bolster privacy and security in IoHT.
“Researchers are exploring a variety of PETs, including anonymization techniques, cryptographic methods, and blockchain technology, to bolster the privacy and security of data in the IoHT ecosystem [21].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [21]
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References 124 · Cited by 51
Research works printed but not linked: 1
- Parra-Arnau, J.; Rebollo-Monedero, D.; Forné, J. Privacy-enhancing technologies and metrics in personalized information systems. In Advanced Research in Data Privacy; Springer: Berlin/Heidelberg, Germany, 2014; pp. 423-442.
- Authors
- Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Han, M., Zhao, L., Sannino, G., Ranieri, C. M., Ueyama, J., & Batista, D. M.
- Affiliations
- Universidade de São Paulo (BR)
- Kennesaw State University (US)
- Macquarie University (AU)
- Zhejiang University (CN)
- Institute for High Performance Computing and Networking (IT)
- Corresponding author
- Mosaiyebzadeh, Fatemeh
- ORCID
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2023-06-16
- Volume
- 12
- Issue
- 12
- Pages
- 2703
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4380987026
- Cited by
- 51
- References
- 124
- Citations by year
- 2026: 17 · 2025: 20 · 2024: 6 · 2023: 8
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DigitalCommons - Kennesaw State University (Kennesaw State University) (submittedVersion)
- CNR ExploRA (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Funders
- CNPq (465446/2014-0, 001, 14/50937-1, 15/24485-9, 2021/10921-2, 2013/07375-0)
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (465446/2014-0, 001, 14/50937-1, 15/24485-9, 2021/10921-2, 2013/07375-0)
- FAPESP (465446/2014-0, 001, 14/50937-1, 15/24485-9, 2021/10921-2, 2013/07375-0)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- privacy-enhancing technologies, federated learning, Internet of Medical Things, wearable medical devices, healthcare data privacy, data security, privacy guarantees, HIPAA, GDPR
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