Bubbles of Trust: A decentralized blockchain-based authentication system for IoT
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ML-based privacy preservation is mostly used due to its efficiency, and federated learning is the more preferable technique.
“Nowadays, ML-based privacy preservation is mostly used due to its efficiency [34], and the more preferable technique is federated learning (FL).”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [34]
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- Authors
- Hammi, M. T., Hammi, B., Bellot, P., & Serhrouchni, A.
- Affiliations
- ParisTech (FR)
- Corresponding author
- Hammi, Badis
- ORCID
- Hammi, Badis
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 78
- Pages
- 126-142
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W2810652557
- Cited by
- 691
- References
- 56
- Citations by year
- 2026: 23 · 2025: 66 · 2024: 69 · 2023: 101 · 2022: 130 · 2021: 127 · 2020: 100 · 2019: 66 · 2018: 9
- FWCI
- 121.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Security (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Authentication Protocols Security
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things security, decentralized authentication, blockchain, device authentication, data integrity, Ethereum, IoT device identification
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